DataGen: Unified Synthetic Dataset Generation via Large Language Models
Under review as a conference paper at ICLR 2025

DATAGEN: UNIFIED SYNTHETIC DATASET VIA LARGE
LANGUAGE MODELS

Anonymous authors
Paper under double-blind review

ABSTRACT

Large Language Models (LLMs) such as GPT-4 and Llama3 have significantly
impacted various fields by enabling high-quality synthetic data generation and
reducing dependence on expensive human-generated datasets. Despite this, chal-
lenges remain in the areas of generalization, controllability, diversity, and truthful-
ness within the existing generative frameworks. To address these challenges, this
paper presents DATAGEN, a comprehensive LLM-powered framework designed to
produce diverse, accurate, and highly controllable datasets. DATAGEN is adaptable,
supporting all types of text datasets and enhancing the generative process through
innovative mechanisms. To augment data diversity, DATAGEN incorporates an
attribute-guided generation module and a group checking feature. For accuracy, it
employs a code-based mathematical assessment for label verification alongside a
retrieval-augmented generation technique for factual validation. The framework
also allows for user-specified constraints, enabling customization of the data gener-
ation process to suit particular requirements. Extensive experiments demonstrate
the superior quality of data generated by DATAGEN, and each module within
DATAGEN plays a critical role in this enhancement. Additionally, DATAGEN is
applied in two practical scenarios: benchmarking LLMs and data augmentation.
The results indicate that DATAGEN effectively supports dynamic and evolving
benchmarking and that data augmentation improves LLM capabilities in various
domains, including agent-oriented abilities and reasoning skills.

1

INTRODUCTION

Large Language Models (LLMs) such as GPT-4 (OpenAI, 2023a), Claude (Anthropic, 2023), and
Llama3 (Meta, 2023) have demonstrated excellent performance across various professional domains,
including medical (Liu et al., 2023a; Zhang et al., 2024a), educational (Kasneci et al., 2023), software
engineering (Qian et al., 2023), and social sciences (Li et al., 2024a;b), as well as in LLM-based
agent applications (Huang et al., 2023a; Liu et al., 2023b; Chen et al., 2024a). Given their superior
generative capabilities, it is natural for researchers to explore effective methods for utilizing these
models in synthetic data generation (Zhu et al., 2024a;b; Wang et al., 2024a). The primary goal is
to produce high-quality, cost-effective datasets, thereby reducing the reliance on expensive human
labor. Furthermore, data generated by LLMs can be utilized for data augmentation (Yu et al., 2024),
dynamic evaluation (Zhu et al., 2024a;b), and model self-alignment (Sun et al., 2023).

Despite the advancements in LLM-driven data generation(Zhu et al., 2024a;b; Wang et al., 2024a;
Dekoninck et al., 2024a;b), which have significantly improved the data generation pipeline and
reduced the human cost, some challenges remain: (1) Generalization and Controllability: Most of
existing frameworks directly modify data items in original datasets in specific ways based on fixed
principles (Zhu et al., 2024b; Wang et al., 2024a) (e.g., add additional context or shuffle the order of
the options), which may constrain the generalization of the generated data as they do not modify the
nature of the data items like the scenarios within items. Moreover, many of them are also limited
to particular dataset formats or types (Yu et al., 2024; Zhu et al., 2024a), such as multiple-choice
or mathematically-oriented datasets (e.g., GSM8K (Cobbe et al., 2021)). Additionally, the lack of
provisions for incorporating external constraints, like specific user requirements (e.g., users may
specify the length of generated text), restricts their controllability during generation. (2) Diversity
and Truthfulness: Prior efforts always overlook the need to ensure some quality aspects of the
datasets like diversity and truthfulness. For instance, the direct application of LLMs for dataset

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Under review as a conference paper at ICLR 2025

Table 1: Comparison of different dataset generation frameworks. The gray checkmark means the
work may achieve parts of the goal (not all).

Related
Work

DyVal (Zhu et al., 2024a)
DyVal 2 (Zhu et al., 2024b)
S3Eval (Lei et al., 2024)
Yu et al. (2024)
Chung et al. (2023)
Fan et al. (2024)
Jandaghi et al. (2023)
Wang et al. (2024a)
MetaMath (Yu et al., 2023)
Qameleon (Agrawal et al., 2023)
Viswanathan et al. (2023)
Chen et al. (2024b)
Gandhi et al. (2024)
DATAGEN (Ours)

General Control. Diversity Truthful w/o Human
-ization

Data
Intervention Knowledge Benchmark Aug.

Dynamic

-lability

-ness

New

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generation often leads to replication and low diversity, as LLMs may output the same answers when
faced with semantically similar input. Furthermore, the propensity of LLMs to produce hallucinations
(Huang et al., 2023b; Sun et al., 2024a) can introduce factual inaccuracies, potentially degrading
model performance when such datasets are used for training or fine-tuning.

To address these challenges, this paper puts forward DATA-
GEN (as shown in Figure 1), a unified and LLM-powered
framework designed to generate a dataset. DATAGEN
ensures the generalization, diversity, truthfulness, and con-
trollability simultaneously of the generation process, com-
pared to previous studies (as shown in Table 1). DATAGEN
accepts all kinds of text datasets and generates high-quality
datasets based on various modules. To enrich the diver-
sity of the generated datasets, DATAGEN employs a range
of strategies, including various hyperparameter settings,
attribute-guided generation, and group checking. To guarantee the truthfulness of the generated
datasets, we propose a code-based mathematical assessment to detect and rectify potentially incorrect
labels. Additionally, we adopt a Retrieval-Augmented Generation (RAG)-based validation method
to check the factuality of generated statements to ensure their truthfulness. DATAGEN integrates
constraints input to align with user specifications to enhance user control over the dataset generation
process. Furthermore, by employing attribute-guided generation and difficulty enhancement, we
enable the generation of data covering a wide range of topics while providing users with controllable
difficulty levels.

Figure 1: Our proposed DATAGEN for
dataset generation via LLMs.

To summarize, the key contributions of this paper are as follows:

• We introduce DATAGEN, a unified framework for generating textual datasets via LLMs, which
accepts the original dataset, description, and user constraints, and integrates modules to ensure
diversity, truthfulness, and controllability.

• We extensively evaluate DATAGEN across data characterization, module efficacy, human evaluation,
error analysis, and cost analysis, confirming its proficiency in dataset generation and highlighting
promising future research directions.

• We explore two applications of DATAGEN: benchmarking LLMs and data augmentation. Key
insights include: I) Most LLMs struggle with math-oriented datasets generated by DATAGEN (e.g.,
GSM8K). II) The performance of LLMs varies significantly across datasets generated by different
LLMs. III) LLMs’ capabilities across various aspects (e.g., agent-related abilities, reasoning
skills) can be improved by fine-tuning based on the generated data. IV) An improvement of data
augmentation exists in knowledge-intensive datasets.

• Based on the observations and findings presented, we discuss the limitations of the current frame-
work for dataset generation and proposes potential improvement measures for future studies. These

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· General. · Control.     · Diversity · Truthful.OriginaldatasetDataset descriptionLarge Language ModelsGenerated datasetGeneration HintInternal EvaluationPost-ProcessingConstraintsUnder review as a conference paper at ICLR 2025

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Figure 2: The architecture of DATAGEN.

enhancements are considered from multiple perspectives, including error analysis, downstream
applications, and LLM alignment.

2 DATAGEN FRAMEWORK

In this section, we will introduce the proposed DATAGEN, a unified framework for dataset generation.
DATAGEN consists of four modules (as shown in Figure 2) including framework input, generation
hint, internal evaluation, and post-processing. Formally, consider an original dataset D, the proposed
framework F, which operates by iteratively sampling subsets Si from D (i.e., example selection for
few-shot learning in subsection 2.2). For each subset, F applies transformations based on the dataset’s
description M(D) and a set of constraints C. The final generated dataset, Dgen, is accumulated over
N iterations: Dgen = (cid:83)N
i=1 F(Si, M(D), C). During generation, the objectives of DATAGEN focus
on maximizing the generalization, controllability, diversity, and truthfulness of the generated dataset.

2.1 FRAMEWORK INPUT

The input for DATAGEN comprises three components: base dataset, dataset description, and genera-
tion constraints: The base dataset is provided in a standardized JSON format, which may include text
with a label or standalone text (e.g., “text with a label” or “single text”). The dataset description artic-
ulates the specifics of the base dataset at a high level, furnishing foundational guidance for the LLM
to synthesize a dataset analogous to the original. While optional, the generation constraints (Zhou
et al., 2023) specify fine-grained conditions under which the LLM operates during dataset generation.
For instance, constraints might stipulate that “Do not generate text longer than 20 words” or “Include
an emoji in each generated sample”, thereby restricting specific conditions of the synthetic dataset.

2.2 GENERATION HINT

Few-Shot Learning. The base dataset typically comprises hundreds of data items; however, incorpo-
rating all these items directly into the prompt may result in an excessively long context that could
obscure the comprehension capabilities of LLMs and incur substantial costs (Bai et al., 2023). To mit-
igate these challenges, few-shot learning techniques are employed for dataset generation (Brown et al.,
2020; Wang et al., 2020). Within DATAGEN, two principal methods are utilized to select few-shot
learning examples. The first method involves a random sampling from the base dataset, effectively
reducing both generation time and associated costs. The second method focuses on enhancing the
diversity of examples, thereby guiding LLMs to generate as varied a dataset as possible. Specifically,
DATAGEN initially encodes all data items using OpenAI’s text-embedding-ada-002 (OpenAI,
a) to create an embedding list. Subsequently, a clustering algorithm (e.g., K-means (Hartigan and
Wong, 1979)) is applied to form n clusters, where n represents the desired number of examples. One
example is randomly selected from each cluster, yielding a set of n diverse examples.

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Diversity Setting. To augment the diversity of the generated data, we implement two strategies:
(1) Hyperparameter Setting. The content generated by LLMs is influenced by various factors, with
hyperparameters such as temperature, top-k, and top-p being crucial. To maximize the diversity of
the dataset, we manipulate these hyperparameters, particularly the temperature settings. (2) Attribute-
Guided Generation. Drawing on insights from prior research (Huang et al., 2023a; Yu et al., 2024),
we formalize the attribute-guided text generation process for LLMs. Let A = {a1, a2, . . . , an} be
a set of attributes, such as "economics" and "sports", intended to guide the generation process. We
model the generation process as a function where the output text y is a function of the input prompt x
and a vector of attributes a ∈ A. The generation process can be expressed as y = P (x, a), where P
represents the generation model of the LLM, and x is the input prompt. To implement this, we employ
two distinct strategies: the first involves directly incorporating user-input customized attributes, and
the second requires asking LLMs to extract necessary attributes from given data examples (the prompt
template is shown in section 13). (3) Group Checking. To ensure diversity among the generated items,
a similarity matrix is employed to identify and filter out pairs of data items exhibiting high similarity.
Further details on this process are provided in subsection 2.4.

2.3

INTERNAL EVALUATION

Overall Quality Assessment and Enhancement. After obtaining the raw generated data, it’s
important to enhance their overall quality as during the generation, LLMs may overlook some
details so as to mistake like deviating from the dataset description. Inspired by recent studies about
self-evaluation and self-alignment (Ji et al., 2023; Ren et al., 2023; Huang et al., 2023c; Jain et al.,
2023; Sun et al., 2023; Wang et al., 2023), we leverage LLMs themselves to improve the quality of
generated data. The process involves two primary steps: (1) Self-Reflection. Each generated data item
is initially subjected to a self-reflection phase, wherein LLMs assess the item to determine errors
and potential areas for enhancement. The output of self-reflection contains two parts: whether the
given data needs to be enhanced and the reason why it needs enhancement. (2) Self-Enhancement.
When LLMs recognize the necessity for improvements, both the reflective insights and the data item
itself are re-input into the LLM to generate an improved version. By establishing a threshold for the
number of iterations and repetitively applying these steps, DATAGEN effectively elevates the overall
quality of the generated items.

Code-Based Mathematical Evaluation. In generating mathematics-related datasets, such as GSM8K
(Cobbe et al., 2021), it has been observed that a proportion of generated labels are factually incorrect.
To address this issue, we employ a code-based mathematical evaluation method to verify the accuracy
of generated labels. As highlighted in the recent study by (Gou et al., 2024; Chen et al., 2023), the
use of tools (e.g., a Python function) can substantially improve reasoning performance. Motivated by
this finding, we require the LLM to generate Python code to solve the given math-related problem.
The code is then executed within a simulative environment to produce a solution. The code-verified
answer(i.e., label) is subsequently compared with the original LLM-generated answer. If they conflict,
the original LLM-generated answer will be replaced with the code-verified answer.

Truthfulness Validation by RAG. Ensuring the truthfulness of generated golden answers is cru-
cial when creating datasets that require factual knowledge. Prior studies have utilized Retrieval-
Augmented Generation (RAG) to enhance the factuality and reduce the incidence of hallucinations in
LLMs (Aksitov et al., 2023; Li et al., 2024c; 2022; Gao et al., 2024). To combat hallucinations within
the generated data, we implement a RAG-based validation process in DATAGEN. Specifically, the
LLM first identifies keywords from the generated text. Subsequently, DATAGEN retrieves relevant
descriptions based on these keywords from the Wikipedia database, as demonstrated in prior research
(Semnani et al., 2023). These descriptions are then used as prompts to guide the LLM in detecting
and correcting any discrepancies or errors in the generated content.

2.4 POST-PROCESSING

Difficulty Enhancement. Given that the dataset is produced by LLMs, the complexity of the
generated data is occasionally insufficient to challenge LLMs as their capabilities evolve. To address
this, and inspired by prior research (Wang et al., 2024a; Zhu et al., 2024b), we implement several
strategies to increase the data’s difficulty. These strategies are designed to elevate the challenges faced
by LLMs in processing and responding to the data. The applied policies include: (1) Paraphrasing

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(a) Len. distribution of generated and original data.

(b) Self-BLEU of generated and original data.

Figure 4: Length and the self-BLEU score of generated data and original data.

Question: Reformulate the phrasing to express the same idea with greater sophistication. (2) Adding
Extra Context into Question: Integrate additional context or details that, while not directly aiding in
the question’s resolution, enhance the question’s complexity. (3) Paraphrasing The Choices: Each
option should be rephrased to reflect the same concept or idea as the original. The essence and
meaning must be preserved. If an option cannot be paraphrased without altering its meaning, it
should remain unchanged. (4) Adding A New Choice: Introduce a plausible but incorrect option to
the existing choices to create ambiguity and require deeper understanding.

Group Checking. To mitigate the issue of high similarity among generated data items, a group-
checking mechanism is implemented to identify and eliminate duplicates. Specifically, we utilize
OpenAI’s text-embedding-ada-002 (OpenAI, a) to compute embeddings for all generated
items. Let X = {x1, x2, . . . , xn} be the set of generated data items, and ei be the embedding of
item xi computed via text-embedding-ada-002. We define the similarity matrix S where the
k=1(eik − ejk)2, representing the Euclidean distance between the
element sij is given by sij =
embeddings of items xi and xj. Data items exhibiting a similarity exceeding a predefined threshold θ
are filtered out to ensure diversity within the dataset. Formally, if sij < θ for any pair (i, j), at least
one of the items xi or xj is randomly removed from the final dataset.

(cid:113)(cid:80)d

3 EXPERIMENTS AND APPLICATIONS

3.1 EXPERIMENTAL SETUP

Type

GSM8K MMLU TruthfulQA HellaSwag

Generated
Original
∆

0.744
0.663
0.681
0.746
2.64% 0.27%

0.743
0.745
0.27%

0.680
0.742
8.36%

Table 2: Remote-Clique of generated data and original data.
∆ is the difference between them.

Figure 3: Human evaluation of overall
quality assessment and enhancement.

To thoroughly evaluate the effectiveness of DATAGEN, we carefully select four representative
benchmark datasets: GSM8K (Cobbe et al., 2021), TruthfulQA (Lin et al., 2022), MMLU (Hendrycks
et al., 2021a), and HellaSwag (Zellers et al., 2019). Each dataset uniquely contributes to language
model assessment, covering dimensions from mathematical problem-solving and factual accuracy
verification to extensive language understanding and commonsense reasoning. We show the details of
these four datasets in section 7. For dataset generation, we utilize GPT-4 (OpenAI, 2023a), Claude3-
Opus (Anthropic, 2023), and Llama3-70b (Meta, 2023), as these LLMs are among the most robust
available, exhibiting exceptional ability to follow instructions. For benchmarking, our study utilizes
eight popular models from notable entities in the AI domain (the details are shown in section 7.),
reflecting a mix of open-source and proprietary technologies. The number of generated data items and
more details are shown in section 9. Note that difficulty enhancement is not applied to the generated
data for benchmarking. We will discuss the effectiveness of difficult enhancement in subsection 3.3.
All LLMs utilized for generation share the same prompt templates.

5

GSM8KHellaSwagMMLUTruthfulQA0100200300Word CountOriginalGeneratedGSM8KHellaSwagMMLUTruthfulQA0.00.40.81.2Self-BLEU ScoreOriginalGeneratedTruthfulQAMMLU0.000.250.500.751.00Percentage (%)Reflection Quality (Better)Reflection Quality (Worse)Case Quality (Better)Case Quality (Worse)Under review as a conference paper at ICLR 2025

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Figure
embedding
text-embedding-ada-002 (OpenAI, a) to obtain text embedding.

Semantic

different

datasets.

of

5:

We

use OpenAI’s

3.2 CHARACTERIZING GENERATED DATA

Length. As depicted in Figure 4a, the length distribution of all generated datasets approximates
a normal distribution. Notably, except for the HellaSwag dataset (as the length of the original
HellaSwag dataset looks like a bimodal distribution), the distributions of other datasets closely
resemble those of their original datasets. This similarity indicates that DATAGEN effectively mimics
the distribution of the original data, thereby enhancing the reliability of the generated datasets.

Semantic Embedding. As illustrated in Figure 5, the distribution of the generated dataset is
encompassed within the distribution of the original dataset. This observation indicates that the data
items generated are semantically aligned with the original data, confirming their semantic correctness.

Diversity. Analogous to the length distribution, the distribution of the self-BLEU score (Zhu et al.,
2018) (as depicted in Figure 4b)—a metric employed to assess text diversity—indicates that the
diversity of the generated data closely aligns with that of the original dataset. This alignment
underscores the exceptional capability of DATAGEN to replicate the diversity inherent in the original
dataset, demonstrating its effectiveness in producing varied textual content. Additionally, we utilize
the remote-clique metric, as applied in prior research (Cevallos et al., 2018), to measure the diversity
of the generated data. The related statistics are presented in Table 2. Observations reveal that the
remote-clique scores of the original and generated data are closely matched, with less than 10%
variance, affirming that our generated data maintains a high level of diversity comparable to the
original dataset.

Knowledge Richness Introduced. In contrast to prior research (Zhu et al., 2024a;b; Wang et al.,
2024a), DATAGEN innovates by generating entirely new data items, rather than merely modifying
existing answers. This approach introduces novel scenarios and knowledge. We assess the knowledge
richness of the data generated by DATAGEN and compared it to the previous study (i.e., Dyval2
(Zhu et al., 2024b)) by calculating the entity overlap rate—how many entities appear both in the
generated and original data. A lower overlap rate indicates that the framework is introducing more
new knowledge. According to our findings, presented in Table 3, DATAGEN demonstrates an average
overlap rate of only 3.83%, significantly lower than that of Dyval2 (Zhu et al., 2024b). This substantial
reduction in overlap rate signifies that our framework excels at incorporating new knowledge into the
generated datasets.

Influence of temperature. We examine the impact of temperature settings on the diversity of data
generated by GPT-4. For this purpose, we select a few items from the TruthfulQA dataset to use as
examples in few-shot learning. We conduct experiments using temperature settings of 0 and 1. Our
findings indicate that the Remote-Clique score (Cevallos et al., 2018) at a temperature of 0 is 0.683,
whereas, at a temperature of 1, it increases to 0.721. This suggests that adjusting the temperature
setting can significantly enhance the diversity of the generated data.

3.3 EFFECTIVENESS OF MODULES IN DATAGEN

In this section, we validate the effectiveness of modules in DATAGEN. To simplify the analysis, our
evaluation is based on the GPT-4 generated data: (1) Diversity Setting. As demonstrated in Table 4,
the DATAGEN modules significantly enhance the diversity of the generated data. Specifically, the
remote-clique score of the initially generated data stands at 0.695. However, the introduction of

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Table 3: The knowledge richness comparison between different principles in DyVal 2 (Zhu et al.,
2024b) and DATAGEN. The principle 1, 2, 3, and 4 are paraphrasing questions, paraphrasing choices,
adding extra context to questions, and adding a new choice.

Baseline

HellaSwag MMLU

TruthfulQA

Avg.

DyVal2-prin.1
DyVal2-prin.2
DyVal2-prin.3
DyVal2-prin.4

24.30%
40.50%
27.00%
51.40%

61.30%
65.70%
62.70%
71.00%

DATAGEN

5.40%

3.30%

51.40%
46.20%
57.30%
47.60%

2.80%

45.67%
50.80%
49.00%
56.67%

3.83%

Figure 6: The percentage of different epoch counts in four datasets.

attribute-guided generation elevates the remote-clique score to 0.735. Furthermore, the implementa-
tion of group checking further increases this score to 0.743. (2) Overall Quality Assessment and
Enhancement. To evaluate the effectiveness of our quality assessment and enhancement module,
we conducted human evaluations focusing on two key aspects: (I) Comparing the quality between
original and enhanced data items; (II) Assessing the reasonableness of the reflections. As illustrated
in Figure 3, the results indicate that almost all reflections were deemed reasonable by the evaluators.
Furthermore, over 80% of the enhanced data items were rated as superior in both datasets. These
findings underscore the effectiveness of our module. (3) Difficulty Enhancement. As demonstrated
in Table 6, it is observable that the performance of most of the LLMs declined when compared to
their performance on the baseline-generated datasets after the application of difficulty enhancement.
This result underscores the effectiveness of difficulty enhancement, which suggests its potential utility
in preventing data contamination (Dong et al., 2024; Golchin and Surdeanu, 2024; Xu et al., 2024).
Such techniques may thus contribute significantly to improving the robustness of LLMs against
overfitting to training datasets. (4) Code-Based Mathematical Evaluation. As depicted in Table 4,
our code-based evaluation methodology has significantly enhanced the correctness of the generated
data, improving from an initial accuracy of 44% to 92%. (5) Truthfulness Validation by RAG. As
detailed in Table 4, the RAG-based validation corrected 4.2% of the examples, demonstrating its
effectiveness. This percentage also highlights the high quality of the dataset generated by GPT-4,
which contains only a few errors. The correctness of (4) and (5) are also manually evaluated, which
of the details can be found in section 8.

In section 10, we investigate the impact of temperature settings on data diversity and evaluate
the adherence of LLMs in DATAGEN to user constraints. Our findings reveal that adjusting the
temperature setting enhances the diversity of generated data. Furthermore, LLMs within DATAGEN
effectively follow user-imposed constraints in both individual and combined scenarios. We also
provide a cost analysis of DATAGEN in section 10, demonstrating that DATAGEN generates datasets
at a significantly low cost.

Table 4: Effectiveness of each module in DATAGEN.

Diversity Enhancement

Code-based.

RAG Validation

Raw

+Attribute Guided +Group Checking Raw +Validation Corrected Percentage

0.695

0.735 (5.8% ↑)

0.743 (6.9% ↑)

44%

88%

4.2%

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123455+Epoch Count0204060Percentage (%)GSM8K123455+Epoch CountPercentage (%)HellaSwag123455+Epoch CountPercentage (%)TruthfulQA123455+Epoch CountPercentage (%)MMLUUnder review as a conference paper at ICLR 2025

Error Type

GSM8K HellaSwag MMLU TruthfulQA

Factuality Error
Format Error
Multiple Answers
Question Error

41%
20%
0%
39%

14%
29%
43%
14%

69%
8%
0%
23%

79%
0%
0%
21%

Table 5: Proportion of different errors. Multiple answers
mean the question is considered to have multiple correct
answers after human evaluation. Question errors mean
the question has quality flaws like unclear statements.

Figure 7: Performance of human and
the best LLM (SOTA LLM) on four
generated datasets.

3.4 HUMAN PERFORMANCE ON GENERATED DATASET

As depicted in Figure 7, the performance comparison between humans and LLMs reveals distinct
outcomes across various datasets. In the HellaSwag dataset, human performance slightly surpasses
that of LLMs. However, in the other three datasets, LLMs demonstrate superior performance. Notably,
in the GSM8K dataset, the accuracy of human responses is lower than that of the best-performing
LLM. For the TruthfulQA and MMLU datasets, which require extensive knowledge, humans perform
significantly worse than LLMs, which benefit from training on large, diverse corpora. More details
about evaluating human performance are shown in section 8.

3.5 ERROR ANALYSIS

To examine the errors present in the generated dataset, we conducted a human evaluation for error
analysis. We observe significant factuality errors in datasets such as GSM8K, TruthfulQA, and
MMLU, primarily because these datasets contain responses that are fact-based (e.g., arithmetic
question answers). This observation underscores the necessity for enhancements in the accuracy of
provided answers. Despite the robust instruction-following capabilities of GPT-4, it occasionally
struggles with data formatting issues. Such errors could be mitigated through clearer prompts or by
employing an integrated framework like LangChain1. Additionally, our analysis of the HellaSwag
dataset revealed the presence of multiple viable answers for certain prompts, highlighting the need
for a more comprehensive answer validation mechanism. We discuss the potential improvement by
mitigating these errors in section 5.

3.6 COST ABLATION ANALYSIS

We conduct a cost analysis of DATAGEN. Specifically, we calculate the total token usage and the
corresponding cost for generating data across four datasets: MMLU, HellaSwag, TruthfulQA, and
GSM8K. The details are presented in Figure 8.

For a generated item without RAG-based validation and code-based evaluation, the cost is at most
$0.038 using the GPT-4-Turbo API. When incorporating RAG-based validation, the average cost per
generated item increases to $0.190, due to the large volume of tokens processed from the retrieved
content. Adding code-based evaluation raises the cost to $0.040. Overall, the total cost for generating
each item, including all validation and evaluation processes, will not exceed $0.200. This cost,
although significant, is substantially lower than the cost of human labor.

Figure 8: Cost (dollar) on different epoch numbers of overall quality assessment and enhancement
(Left), and the token number cost of each part in DATAGEN.

1https://github.com/langchain-ai/langchain

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Table 6: LLMs’ performance on baseline generated (i.e., gen.) dataset, challenge or difficulty
enhanced dataset (i.e., cha.), and their differences (i.e., diff.).

Model

GSM8K

MMLU

HellaSwag

TruthfulQA

gen.

cha.

diff.

gen.

cha.

diff.

gen.

cha.

diff.

gen.

cha.

diff.

ChatGPT
Claude-3
Llama3-70b
Llama3-8b
Mistral-7b
Mixtral-8x7b
Yi-34b

0.665
0.778
0.689
0.613
0.377
0.509
0.637

0.585
0.670
0.637
0.557
0.321
0.439
0.509

0.080
0.108
0.052
0.056
0.056
0.070
0.128

0.798
0.903
0.857
0.741
0.709
0.851
0.815

0.633
0.725
0.703
0.576
0.437
0.616
0.633

0.165
0.178
0.154
0.165
0.272
0.235
0.182

0.960
0.935
0.949
0.793
0.696
0.511
0.572

0.924
0.880
0.884
0.699
0.467
0.373
0.522

0.036
0.055
0.065
0.094
0.229
0.138
0.050

0.816
0.919
0.914
0.795
0.738
0.824
0.857

0.718
0.810
0.743
0.676
0.452
0.648
0.657

0.098
0.109
0.171
0.119
0.286
0.176
0.200

Table 7: The main results on generated datasets (i.e., gen.) and original datasets (i.e., ori.).

Dataset

ChatGPT
Claude-3
Llama3-70b
Llama3-8b
Mistral-7b
Mixtral-8x7b
Yi-34b

ChatGPT
GPT-4
Llama3-70b
Llama3-8b
Mistral-7b
Mixtral-8x7b
Yi-34b

GSM8K

MMLU

TruthfulQA

HellaSwag

ori.

gen.

ori.

gen.

ori.

gen.

ori.

gen.

GPT-4 Generation

0.762
0.953
0.890
0.800
0.313
0.610
0.687

0.762
0.947
0.890
0.800
0.313
0.610
0.687

0.665
0.778
0.689
0.613
0.377
0.509
0.637

0.405
0.508
0.444
0.367
0.158
0.291
0.323

0.798
0.903
0.857
0.741
0.709
0.851
0.815

0.609
0.810
0.755
0.565
0.490
0.720
0.645
Claude-3-Opus Generation
0.609
0.725
0.755
0.565
0.490
0.720
0.645

0.802
0.848
0.846
0.780
0.709
0.717
0.751

0.825
0.855
0.750
0.450
0.382
0.640
0.485

0.432
0.841
0.750
0.450
0.380
0.640
0.480

0.837
0.919
0.914
0.795
0.738
0.824
0.857

0.744
0.888
0.854
0.709
0.621
0.680
0.694

0.611
0.888
0.836
0.684
0.600
0.712
0.740

0.538
0.736
0.836
0.568
0.580
0.600
0.644

0.960
0.935
0.949
0.793
0.696
0.511
0.572

0.712
0.835
0.769
0.704
0.690
0.565
0.584

3.7 APPLICATION-I: BENCHMARKING LLMS

We present the benchmarking results based on GPT-4 and Claude3 generated data for seven pop-
ular LLMs in Table 7 (the benchmarking results based on Llama3-70b’s generation are shown in
section 10). The analysis yields several key observations:

• Performance decline on generated GSM8K dataset: Almost all LLMs exhibit a performance
drop on the generated GSM8K dataset compared to the original. This suggests that the reasoning
capabilities of many LLMs may be overstated, aligning with recent findings (Zhang et al., 2024b;
Mirzadeh et al., 2024; Zhang et al., 2024b), which indicate overfitting on the GSM8K dataset by
some LLMs.

• Superior performance on knowledge-required datasets: For datasets requiring extensive knowl-
edge, such as MMLU and TruthfulQA, LLMs achieve higher accuracy on the generated versions.
This indicates that the knowledge necessary to address these queries is within the LLMs’ capabili-
ties, suggesting that the generated datasets are relatively less challenging. Further enhancements to
increase difficulty are detailed in Table 6.

• Challenging nature of Claude3-generated dataset: LLMs generally perform worse on datasets
generated by Claude3 compared to those by GPT-4. This may imply that some LLMs might have
been trained or augmented with GPT-4 generated data (e.g., Phi-3 (Abdin et al., 2024)), highlighting
the unique challenge of Claude3-generated content.

3.8 APPLICATION-II: DATA AUGMENTATION

Using data augmentation with LLMs has been widely explored in previous studies (Dai et al., 2023;
Whitehouse et al., 2023; Møller et al., 2024). In this section, we implement our DATAGEN to augment
data in ten popular datasets (the details of datasets are shown in section 7). We include the experiment
setting in section 9. From Figure 9, we can observe that:

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Figure 9: Results of data augmentation on Llama2-7b, Llama3-8b and Mistral-7b.

Table 8: Model performance scores in MTBench.

Model

First Turn Score

Second Turn Score Average Score

llama3-7b-base
llama3-7b-alpaca-original
llama3-7b-alpaca-genset

2.325
6.049
6.981

1.744
4.450
5.825

2.038
5.259
6.403

• The data augmentation powered by DATAGEN is effective. Performance across all ten datasets
improved when trained with the DATAGEN-generated dataset, highlighting the efficacy of our
generated data and indicating broader potential applications for DATAGEN across extensive datasets.
• DATAGEN enhances LLMs from various capability aspects. The enhancements in various
aspects of LLM capabilities due to the generated data are notable. For example, performance
improvements in the Metatool dataset (Huang et al., 2023a) (i.e., tool selection ability) indicate
that DATAGEN can enhance agent-oriented capabilities of LLMs. Additionally, enhancements in
reasoning abilities are evident in datasets such as GSM8K (Cobbe et al., 2021) and both the BBH
(bool/casual) (Suzgun et al., 2022).

• Improvement on knowledge-intensive datasets still leaves much to be desired. The gains in
datasets requiring extensive knowledge (e.g., TruthfulQA (Lin et al., 2022)) are comparatively
modest. This limited improvement may be due to LLMs acquiring most of their knowledge during
pretraining, and the additional 200 training samples may not significantly impact performance on
related tasks. Notably, the Llama2-7b model shows a performance decline on TruthfulQA after
fine-tuning, possibly due to hallucinations introduced when new knowledge is acquired during
fine-tuning rather than pretraining (Gekhman et al., 2024). We discuss the potential measurement
for enhancing in section 5.

Moreover, we extend our analysis to include general instruction tuning data. Specifically, we utilize the
alpaca dataset Taori et al. (2023) for additional fine-tuning on the Llama3-base model and evaluated
the outcomes using the MT-Bench Zheng et al. (2023). The “genset” model, fine-tuned on 1,000 data
points generated by DATAGEN, consistently outperforms the “original” model, which is fine-tuned
on an equivalent sample of 1,000 existing data points from the alpaca dataset. This comparison
demonstrates that our framework effectively generates high-quality, diverse instruction-tuning data,
demonstrating its practical utility in enhancing model performance.

4 CONCLUSION

In this paper, we hava proposed DATAGEN, a unified dataset generation framework powered by LLMs,
which addresses key challenges in diversity, accuracy, and controllability. Its innovative modules
and features, ensure high-quality, customizable datasets. The extensive experiments demonstrated
the effectiveness of DATAGEN. Moreover, DATAGEN can be applied in dynamic and evolving
benchmarking as well as data augmentation. We believe that the insightful findings revealed in this
study will serve as a foundation for future research on data generation.

10

VanillaFT0%20%40%60%80%100%MetatoolVanillaFTBBH(casual)VanillaFTBoolQVanillaFTMultiNLIVanillaFTEmoBenchVanillaFTHellaSwagVanillaFTMMLUVanillaFTTruthfulQAVanillaFTGSM8KVanillaFTBBH(bool)VanillaFT0%20%40%60%80%100%VanillaFTVanillaFTVanillaFTVanillaFTVanillaFTVanillaFTVanillaFTVanillaFTVanillaFTVanillaFT0%20%40%60%80%100%VanillaFTVanillaFTVanillaFTVanillaFTVanillaFTVanillaFTVanillaFTVanillaFTVanillaFTUnder review as a conference paper at ICLR 2025

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Appendix

5

IMPACT, LIMITATION, AND IMPROVEMENT

Our proposed framework, DATAGEN, not only reduces the costs associated with manually creating
data and supports dynamic benchmarking and data augmentation but also significantly impacts the
data generation field in several key ways:

• Alleviating resource scarcity. DATAGEN effectively addresses the shortage of low-resource
datasets. For instance, current datasets were predominantly in English, leaving non-English
datasets scarce. Moreover, DATAGEN can help fill the dataset scarcity in some domains, especially
some interdisciplinary fields like AI in psychology (Li et al., 2024a). This is significant for both
domain development and AI fairness.

• Enhancing model robustness. The diversity and challenges presented by data generated through
DATAGEN help models improve their ability to handle complex and varied real-world data. This,
in turn, enhances the models’ generalization capabilities and reliability, especially in scenarios
involving data contamination.

• Expanding research applications. The methodology used in DATAGEN can be adapted for
other modal data generation frameworks. As models capable of handling different modalities
or even multimodal data emerge, the research into data generation for these modalities becomes
increasingly relevant and impactful.

While this research presents notable advancements, it concurrently grapples with certain limitations,
which means we have much more space for improvement.

• From the perspective of error analysis (subsection 3.5). The error analysis identifies primary
areas where DATAGEN can diminish errors to enhance reliability. To address factuality errors,
deploying a robust LLM-based agent (Liu et al., 2023b) enhanced with a broader verification
toolkit—comprising an extensive database and web access capabilities—is crucial. Furthermore,
question errors frequently stem from LLMs’ misinterpretations of dataset descriptions and objec-
tives, a direct consequence of alignment inefficiencies (Ji et al., 2024). Implementing a plug-in
module that refines human-written dataset descriptions into formats more comprehensible to LLMs
could mitigate this issue.

• From the perspective of downstream applications (subsection 3.7 and subsection 3.8): A
significant oversight in our endeavor to establish a universal dataset generation framework was the
insufficient focus on adaptability for specific applications. Concerning dynamic benchmarking
protocols such as DyVal (Zhu et al., 2024a) and DyVal 2 (Zhu et al., 2024b), it is vital to ascertain
the specific capabilities that these benchmarks aim to evaluate. For example, while the GSM8K is
designed to assess reasoning abilities, the current dataset generation paradigm, which leverages
descriptions and few-shot examples, may fail to challenge LLMs adequately. Therefore, orienting
the generation process to explicitly target the capabilities under evaluation could truly enhance the
dynamism of the dataset. Additionally, our findings indicate limited improvements when applying
data augmentation to knowledge-intensive datasets like MMLU (Hendrycks et al., 2021b) and
TruthfulQA (Lin et al., 2022). A more effective approach could involve identifying novel or out-of-
distribution (OOD) data that represents unmastered knowledge for LLMs, thereby significantly
enhancing learning outcomes.

• From the perspective of weak-to-strong alignment (Zheng et al., 2024a; Burns et al., 2023)
& self-alignment (Sun et al., 2023; Li et al., 2023; Sun et al., 2024b): LLM-generated data
have been extensively utilized to improve LLMs themselves. For example, Phi-3 (Abdin et al.,
2024) is trained using a substantial amount of synthetic data generated by GPT-4. This utilization
demonstrates that LLMs can undergo self-evolution through synthetic data. In our study, while we
have explored potential alignments in a cross-model mode (e.g., using GPT-4 to enhance weaker
models), the strategies for self-alignment or weak-to-strong alignment within the same model
are not thoroughly investigated. Future research focusing on how to adapt a dataset generation
framework like DATAGEN for use in data-centric alignment domains will be of considerable
importance.

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6 RELATED WORK

Benchmarking and Evaluating LLMs. Owing to the remarkable capabilities of LLMs, bench-
marking these models is essential for a deeper understanding of both general and specialized do-
mains (Chang et al., 2023). The evaluation of LLMs encompasses a wide range of fields, initiating
with core NLP tasks such as sentiment analysis (Lopez-Lira and Tang, 2023; Zhang et al., 2023a),
text classification (Yang and Menczer, 2023; Zhang et al., 2023b), and natural language infer-
ence (McKenna et al., 2023). A holistic evaluation framework, the HELM benchmark, has been
proposed by Liang et al. (2023), laying the groundwork for comprehensive assessments. Additionally,
the application of LLMs spans diverse sectors (Gu et al., 2023), including computational social
science (Ziems et al., 2023), legal analytics (Nay et al., 2023; Guha et al., 2023; Fei et al., 2023),
and psychological studies (Frank, 2023; Li et al., 2024a). Furthermore, several benchmarks have
been designed to scrutinize trustworthiness dimensions such as safety and privacy in LLMs (Sun
et al., 2024a; Huang et al., 2023d; Zhang et al., 2023c; Wang et al., 2024b; Guan et al., 2024; Zhuo
et al., 2023). Static benchmarks are susceptible to data contamination, wherein developers might
incorporate benchmark datasets into the training data to artificially enhance performance. To mitigate
this, flexible protocols for dynamic evaluation have been advanced, exemplified by the recent ini-
tiatives DyVal (Zhu et al., 2024a) and DyVal 2 (Zhu et al., 2024b). Additionally, Fan et al. (2024)
introduced NPHardEval, featuring monthly updated datasets. The S3Eval framework, a scalable
evaluation suite for LLMs, was conceptualized by (Lei et al., 2024). Moreover, some benchmarks
adopt methodologies where LLMs function as evaluators (e.g., LLM-as-a-judge) (Liu et al., 2023c;
Chen et al., 2024c; Zheng et al., 2023), with AlignBench proposing a multi-dimensional assessment
using this approach (Liu et al., 2023c).

Synthetic Data by LLMs. LLMs have demonstrated an impressive capacity for data generation,
leading to their application in creating synthetic datasets for pretraining and finetuning, replacing
the labor-intensive processes of manual data scraping and selection (Liu et al., 2024). Distinct from
earlier methods that focus on traditional language models (Schick and Schütze, 2021), LLMs offer
enhanced prospects for producing high-quality synthetic data across a wide spectrum of applications,
such as multilingual QA (Riabi et al., 2021), chatbot conversation (Zhao et al., 2023) and data
diversity augmentation (Dai et al., 2023; Chung et al., 2023).

The concept of synthetic benchmarks takes a step further by demanding that the LLM-generated
data be diverse accurate and systematically challenging. For instance, Wang et al. (2024a) devised
a framework that enhances the evolution of benchmarks by applying six reframing techniques on
existing datasets. Wei et al. (2024) employed GPT-4 to create LongFact, comprising extensive QA
pairs that serve as a benchmark for evaluating long-form factual content. Moreover, synthetic bench-
marks have also been constructed in evaluating LLM emergent capabilities such as trustworthiness
(Sun et al., 2024a), tool usage (Huang et al., 2023a; Qin et al., 2023) and persona-based conversation
(Jandaghi et al., 2023). Our research advances synthetic benchmark generation by developing a
paradigm that integrates multiple plug-and-play modules into LLM dataset creation, leveraging
emergent capabilities by various prompting methods (e.g., self-evaluation (Ji et al., 2023)) to produce
data items with high-quality. Recently, in response to concerns about the quality of synthetic datasets,
Dekoninck et al. (2024a) conducted comprehensive experiments to evaluate the diversity and fidelity
of synthetic data produced by LLMs, while Dekoninck et al. (2024b) introduced a new inference
framework, model arithmetic, to control the content generated by LLMs.

7 DETAILS OF DATASETS AND MODELS

7.1 DATASETS

GSM8K. GSM8K is a dataset designed to test the mathematical problem-solving ability of large
language models (Cobbe et al., 2021). It comprises approximately 8,000 math word problems typical
of those in grade school. The problems are diverse, covering various topics and difficulties, making it
a comprehensive tool for assessing the reasoning capabilities of models in numerical contexts.

TruthfulQA. TruthfulQA is a dataset crafted to evaluate the truthfulness and factual accuracy of
answers provided by language models (Lin et al., 2022). It consists of questions that models frequently

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1097
1098

1099
1100
1101
1102
1103
1104

1105
1106
1107
1108
1109
1110

1111
1112
1113
1114
1115
1116

1117
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1120
1121

1122
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1124
1125
1126
1127

1128
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respond to incorrectly or misleadingly. The dataset challenges models on simple factual questions and
questions requiring a nuanced understanding of common misconceptions and controversial topics.

MMLU. MMLU is a large-scale dataset designed to test various language understanding tasks
(Hendrycks et al., 2021b). It covers 57 subjects ranging from humanities to natural sciences, providing
a broad spectrum of topics. This diversity makes MMLU highly effective for assessing the general
knowledge and understanding of language models across varied domains.

HellaSwag. HellaSwag is a dataset that evaluates common sense reasoning and context understanding
in language models (Zellers et al., 2019). It includes scenarios requiring the prediction of the most
plausible continuation among several options. The dataset is crafted to be particularly challenging,
often including subtle nuances and twists that test the depth of contextual comprehension.

MetaTool. MetaTool is a benchmark designed to evaluate LLMs’ awareness and proficiency in tool
usage and selection (Huang et al., 2023a). In our experiment, we conducted evaluations on two tasks.
In our experiments, we specifically focused on single-tool selection.

MultiNLI. The Multi-Genre Natural Language Inference (MultiNLI) is a crowd-sourced dataset of
433k sentence pairs annotated with textual entailment information (Williams et al., 2018). It covers
a range of genres of spoken and written text and supports a distinctive cross-genre generalization
evaluation.

ARC (Challenge). The AI2’s Reasoning Challenge (ARC) dataset is a multiple-choice question-
answering dataset, containing questions from science exams from grade 3 to grade 9 (Clark et al.,
2018). The dataset is split into two partitions: Easy and Challenge, where the latter partition contains
the more difficult questions that require reasoning.

BoolQ. BoolQ is a reading comprehension dataset with questions that are unexpectedly challenging
(Clark et al., 2019). They often query for complex, non-factoid information, and require difficult
entailment-like inference to solve.

BBH. BIG-Bench Hard (BBH) is a subset of the BIG-Bench, a diverse evaluation suite for language
models (Suzgun et al., 2022). BBH focuses on a suite of 23 challenging tasks from BIG-Bench that
were found to be beyond the capabilities of current language models. We select two tasks from BBH:
boolean expressions2 and causal judgement3.

7.2 MODELS

Models for Benchmarking. These include ChatGPT (OpenAI, 2023a) and GPT-4 (OpenAI, 2023b)
by OpenAI (Ope), known for their robust conversational abilities; Llama3-70b and Llama3-8b (Meta,
2023) by Meta AI (Meta), open-source and favored for their versatility across different computational
scales; Mistral-7b and Mistral-8x7b (Jiang et al., 2024) by Mistral AI (OpenAI, b), designed for
efficiency in language tasks; Claude3 (Anthropic, 2023) by Anthropic (Ant), which focuses on safe
and ethical AI interactions; and Yi-34b (AI et al., 2024) by 01.AI (OpenAI, c), a model fine-tuned
using high-quality curated data to ensure helpfulness.

8 DETAILS OF HUMAN EVALUATION

We conduct human evaluations in two parts: effectiveness of each module in DATAGEN (subsec-
tion 3.3) and error analysis (subsection 3.5). Four undergraduate students and one PhD student
with professional English skills carry out these evaluations. Some annotation screenshots of human
evaluation are shown in Figure 13 and Figure 14.

Effectiveness of Each Module in DATAGEN. In subsection 3.3, we conduct the human ablation
evaluation of overall quality assessment and enhancement, code-based, and RAG-based validation.
Specifically, for code-based evaluation, when a label contradicts the code output, we will manually
check whether the code output is correct (in DATAGEN, we will replace the original label with code

2https://github.com/suzgunmirac/BIG-Bench-Hard/blob/main/bbh/boolean_

expressions.json

3https://github.com/suzgunmirac/BIG-Bench-Hard/blob/main/bbh/causal_

judgement.json

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Table 9: The dataset description we used in DATAGEN.

Dataset

Description

HellaSwag

MMLU

GSM8K

TruthfulQA

MetaTool

MultiNLI

ARC-C

BoolQ

BBH (Bool)

This dataset consists of multiple-choice questions designed to test the logical reasoning and contextual understanding
of AI models. Each question sets up a scenario and asks "What happens next?" with four potential answers. Only one
answer is logically sound and contextually appropriate, while the other three are implausible, either contradicting the
scenario’s details or representing unlikely outcomes.The purpose of these questions is to challenge AI models to use
logical sequencing, inferential reasoning, and practical insights effectively. This dataset aims to refine AI abilities in
predicting logical continuations in scenarios that mimic real-life logic and events, ensuring the challenges are complex
and thought-provoking.

It is a large-scale, multi-task language understanding dataset designed to evaluate language models’ capabilities across
various language understanding tasks. The dataset questions are presented in a multiple-choice format, each with a
question (referred to as "text") followed by four options (labeled A, B, C, and D). Each question is associated with a
correct answer ("label")

It is a dataset of high-quality linguistically diverse grade school math word problems created by human problem
writers. These problems take between 2 and 8 steps to solve, and solutions primarily involve performing a sequence
of elementary calculations using basic arithmetic operations (+ − × ÷) to reach the final answer. A bright middle
school student should be able to solve every problem. It can be used for multi-step mathematical reasoning. Each
problem should only have one question and one correct answer.

This dataset is designed to measure the truthfulness and accuracy of answers generated in response to common
questions, some of which are often answered incorrectly by humans due to widespread misconceptions or false beliefs.
The purpose of the dataset is to evaluate how well a model can distinguish factual accuracy from popular myths or
erroneous understandings in various domains including history, science, and general knowledge. Each entry in the
dataset consists of a question followed by multiple-choice answers where only one is correct. The dataset challenges the
model’s ability to use historical data, scientific facts, and logical reasoning to select the correct answer over plausible
but incorrect alternatives that might reflect common misunderstandings.

Each entry in the dataset includes a user’s query and a list of tool options. The model is required to select the most
appropriate tool from the list that can best address the query. The dataset is designed to test the model’s ability to
choose the right tool.

The dataset is a crowd-sourced collection of sentence pairs annotated with textual entailment information. Each data
item contains two different sentences and has the label "neutral", "contradiction", or "entailment".

The dataset is designed to test the model’s ability to understand and correctly answer science questions at a grade-school
level, focusing on assessing capabilities such as comprehension, reasoning, and application of scientific knowledge.
Each entry in the dataset consists of a question followed by multiple-choice answers where only one is correct.

This dataset is a question-and-answer dataset on reading comprehension. Given the title of a passage and the content of
it, it requires providing a "true" or "false" answer to the given question. These questions are unexpectedly challenging
as they often query for complex, non-factoid information and require difficult entailment-like inference to solve.

The dataset consists of Boolean expressions and their respective evaluations. Each entry in the dataset is a pair,
comprising a Boolean expression (as a question) and the expected result (as a label). The Boolean expressions include
combinations of True, False, and, or, and not operators, testing various logical conditions. This dataset is useful for
training models to understand and evaluate Boolean logic.

BBH (Casual)

The dataset contains various scenarios designed to test causal judgment. Each entry includes a scenario described
in detail, followed by a question about the causality involved, and multiple-choice options for answers. The target
indicates the expected answer to the question based on typical causal reasoning.

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Table 10: The size of the generated dataset used in subsection 3.2 and benchmarking LLMs.

GSM8K HellaSwag MMLU TruthfulQA

212

226

193

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output if they contradict). For the RAG-based validation, we also manually whether the correcting
action is reasonable and supported by the ground truth.

Human Performance. The human evaluation was conducted by five students as mentioned before.
Each student completed all questions across four datasets. The final performance scores were then
averaged to obtain a comprehensive measure of human performance.

Error Analysis. The error analysis (subsection 3.5) is based on a structured human evaluation
approach. To ensure the quality of the generated questions, human experts review each question
against specific criteria that cover various aspects of data integrity and logical coherence. Below are
the detailed aspects that are evaluated:

• Data format. This aspect evaluates whether the data presented in the questions adheres to the
expected formats and standards. For example, dates should use a consistent format and options for
generated data should be presented with the correct format (e.g., A, B, C, or D).

• The logicality of mathematical questions. Experts assess whether the mathematical problems
posed in the questions are logically sound and solvable within the given context. This includes
checking for the presence of all necessary information, the feasibility of the operations, and the
logical flow from premises to the conclusion.

• Correctness of answer. This criterion involves verifying that the answers provided or implied by

the questions are correct and accurate.

• Articulation of data items. Reviewers examine how clearly data items are articulated within
the questions. This includes clarity of language, proper grammatical structure, and the logical
arrangement of information to facilitate easy understanding. Ambiguity or miscommunication that
could hinder the respondent’s ability to accurately interpret the question is flagged for correction.

9 DETAILS OF EXPERIMENT SETTING

Dataset Generation. To maximize the consistency of the experimental results, we set the temperature
parameter for both GPT-4 and Claude-3 to 0. The size of the generated dataset used in subsection 3.2
and benchmarking LLMs is shown in Table 10. The batch size of generation (the number of items
generated per time) is set to 5.

Inference Settings. We maintained uniform hyperparameter settings across all models. Specifically,
the model temperature was set to 0 to enhance productivity, and the top-p was set to 1. For bench-
marking purposes with Mixtral-8x7b and Llama3-70b, we utilized the inference API provided by
Replicate4.

Fine-tune Settings. For each dataset, DATAGEN generates 200 samples powered by GPT-4 and then
evaluates the fine-tuned models on the test set of the original dataset. The labels or ground-truth
answers of generated data always contain only a few words, lacking a thinking process that may
be more important for fine-tuning. To address this, the labels or the ground-truth answers of the
generated dataset are refined and extended by GPT-4 itself (e.g., transform the answers into Chain-of-
Thoughts format (Wei et al., 2023)). Then a self-evaluation of GPT-4 will be conducted to ensure
the correctness and accuracy of refined answers. Our fine-tuning is all based on the Supervised
Fine-Tuning (SFT):

LSFT (πθ) = −E(x,y)∼D [log πθ(y | x)]

(1)

We applied the LoRA (Hu et al., 2021) technique to fine-tune Llama3-8b and Mistral-7b. The rank of
LoRA was set to 8, the learning rate was e−5, and we used the Adam optimizer (Kingma and Ba,

4https://replicate.com/

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2017) for training. The models were trained over 5 epochs with a batch size of 4, utilizing mixed
precision training. The training took place on a server equipped with an A100 GPU with 80GB
of VRAM. For the training process, we employed the LLAMA-Factory framework (Zheng et al.,
2024b).

10 ADDITIONAL EXPERIMENT RESULTS

We show the benchmarking results based on the generated data from Llama3-70b in Table 13.
Moreover, we also show the training loss and evaluation loss during fine-tuning for data augmentation
in Figure 10, Figure 11 and Figure 12.

User Constraints. To evaluate the effectiveness of LLMs in DATAGEN at adhering to user-specified
constraints, our assessment is structured into two levels. The first level involves evaluating the model’s
performance under single constraints, while the second level examines performance under combined
constraints. The single constraints assessed include:

• Length-related: (1) Ensure each option is longer than 20 words. (2) Ensure each option is shorter
than 20 words. (3) Ensure each question is longer than 100 words. (4) Ensure each question is
shorter than 100 words.

• Topic-related: (1) Ensure the question is related to sports. (2) Ensure the question is related to

computer science.

• Structure-related: Ensure each question contains five options.
• Language-related: (1) Ensure the questions and options are output in Chinese. (2) Ensure the

questions and options are output in Spanish.

The combined constraints are shown in Table 11.

Table 11: The combined constraint used in the experiments.

NO.

1
2
3
4
5

Constraint 1

Constraint 2

Ensure each option is longer than 20 words.
Ensure each option is less than 20 words.
Ensure each question is longer than 100 words.
Ensure each question contains five options.
Ensure the question and options are output in Chinese.

Ensure each question is less than 100 words.
Ensure each question is longer than 100 words.
Ensure each question contains five options.
Ensure the question is related to Computer and Science.
Ensure the question is related to Computer and Science.

To assess whether the LLM adheres to user-imposed constraints, we utilize the LLM-as-a-Judge
approach (Zheng et al., 2023), a method extensively employed in prior research (Liu et al., 2023c; Gao
et al., 2024). The evaluation prompt details are provided in section 14. As indicated in Table 12, GPT-
4 demonstrates outstanding performance across both single and combined constraints. It achieves a
100% compliance rate in nine out of ten single constraints, illustrating its robust capability to follow
simple and typical user instructions. Although there is a slight performance decline in combined
constraints, GPT-4 consistently maintains adherence to user constraints in most scenarios.

Diversity. For more features of generated data, we have referred to the study (Yu et al., 2024)
to guide our incorporation of two quantitative metrics to evaluate dataset diversity: the Average
Pairwise Sample Similarity (APS) and the Inter-Sample N-Gram Frequency (INGF). Lower APS
values indicate better diversity, whereas higher INGF values signify greater diversity. The result is
shown in Table 14.

Table 12: The GPT-4’s performance on user constraints.

Length-related

Structure-related

Topic-related

Language-related

(1)

(2)

(3)

(4))

(1)

(2)

(1)

(2)

100.00% 96.00%

100.00%

100.00%

100.00%

100.00% 100.00% 100.00% 100.00%

Constraint 1

Constraint 2

Constraint 3

Constraint 4

Constraint 5

Single Constraint (↑), Combined Constraint (↓)

96.67%

83.33%

100.00%

98.00%

100.00%

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Under review as a conference paper at ICLR 2025

Table 13: The main results of eight LLMs on Llama3-70b generated datasets (i.e., gen.) and original
datasets (i.e., ori.).

Model

ChatGPT
Claude-3
GPT-4
Llama3-70b
Llama3-8b
Mistral-7b
Mixtral-8x7b
Yi-34b

GSM8K

HellaSwag

MMLU

TruthfulQA

ori.

0.770
0.805
0.805
0.720
0.685
0.513
0.600
0.725

gen.

0.762
0.953
0.947
0.890
0.800
0.313
0.610
0.687

ori.

0.733
0.895
0.910
0.764
0.805
0.825
0.569
0.785

gen.

0.538
0.888
0.736
0.836
0.568
0.580
0.600
0.644

ori.

0.811
0.775
0.835
0.825
0.760
0.760
0.750
0.805

gen.

0.609
0.810
0.725
0.755
0.565
0.490
0.720
0.645

ori.

0.857
0.915
0.890
0.940
0.840
0.710
0.880
0.830

gen.

0.432
0.855
0.841
0.750
0.450
0.380
0.640
0.480

Table 14: Comparison of Original and Generated APS and INGF values across datasets

Dataset

Original APS Generated APS Original INGF Generated INGF

TruthfulQ&A
GSM8K
MMLU
HellaSwag

0.029
0.053
0.047
0.076

0.091
0.057
0.050
0.089

882.181
3021.619
2185.514
2586.710

1603.976
1296.588
1566.574
2193.623

Figure 10: Training loss and eval loss during Llama2’s fine -tuning.

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50100150Step0.80.91.0Training LossTruthfulQAOriginalSmoothed0100200Step0.60.81.01.2Training LossmultiNLIOriginalSmoothed0100200Step0.60.8Training LossGSM8KOriginalSmoothed50100150Step0.500.751.001.25Training LossBBH(bool)OriginalSmoothed50100150Step0.80.91.01.1Training LossMMLUOriginalSmoothed50100150Step0.60.81.0Training LossmetatoolOriginalSmoothed0100200Step0.81.0Training LossBoolQOriginalSmoothed50100150Step0.80.91.0Training LossARC-COriginalSmoothed50100150Step0.81.01.2Training LossHellaSwagOriginalSmoothed100200Step1.01.5Training LossBBH(casual)OriginalSmoothed50100150Step0.800.850.900.95Eval LossTruthfulQAOriginalSmoothed0100200Step0.81.0Eval LossmultiNLIOriginalSmoothed0100200Step0.60.70.8Eval LossGSM8KOriginalSmoothed50100150Step0.60.81.01.2Eval LossBBH(bool)OriginalSmoothed50100150Step1.01.1Eval LossMMLUOriginalSmoothed50100150Step0.81.0Eval LossmetatoolOriginalSmoothed0100200Step0.81.0Eval LossBoolQOriginalSmoothed50100150Step0.80.9Eval LossARC-COriginalSmoothed50100150Step1.01.21.4Eval LossHellaSwagOriginalSmoothed100200Step1.01.2Eval LossBBH(casual)OriginalSmoothedUnder review as a conference paper at ICLR 2025

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Figure 11: Training loss and eval loss during Llama3’s fine -tuning.

Figure 12: Training loss and eval loss during Mistral’s fine-tuning.

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50100150Step0.80.91.0Training LossTruthfulQAOriginalSmoothed0100200Step0.60.8Training LossmultiNLIOriginalSmoothed0100200Step0.40.5Training LossGSM8KOriginalSmoothed50100150Step0.40.50.6Training LossBBH(Bool)OriginalSmoothed50100150Step0.80.9Training LossMMLUOriginalSmoothed50100150Step0.60.7Training LossmetatoolOriginalSmoothed0100200Step0.70.80.9Training LossBoolQOriginalSmoothed50100150Step0.700.750.800.85Training LossARC-COriginalSmoothed50100150Step0.80.91.0Training LossHellaSwagOriginalSmoothed100200Step0.81.0Training LossBBH(casual)OriginalSmoothed50100150Step0.800.850.90Eval LossTruthfulQAOriginalSmoothed0100200Step0.60.70.8Eval LossmultiNLIOriginalSmoothed0100200Step0.500.55Eval LossGSM8KOriginalSmoothed50100150Step0.450.500.55Eval LossBBH(Bool)OriginalSmoothed50100150Step0.900.951.001.05Eval LossMMLUOriginalSmoothed50100150Step0.60.7Eval LossmetatoolOriginalSmoothed0100200Step0.70.8Eval LossBoolQOriginalSmoothed50100150Step0.750.80Eval LossARC-COriginalSmoothed50100150Step0.91.01.1Eval LossHellaSwagOriginalSmoothed100200Step0.91.0Eval LossBBH(casual)OriginalSmoothed50100150Step0.60.70.80.9Training LossTruthfulQAOriginalSmoothed0100200Step0.40.60.8Training LossmultiNLIOriginalSmoothed0100200Step0.30.40.5Training LossGSM8KOriginalSmoothed50100150Step0.40.6Training LossBBH(bool)OriginalSmoothed50100150Step0.60.70.80.9Training LossMMLUOriginalSmoothed50100150Step0.40.50.6Training LossmetatoolOriginalSmoothed0100200Step0.60.8Training LossBoolQOriginalSmoothed50100150Step0.60.70.8Training LossARC-COriginalSmoothed50100150Step0.60.81.0Training LossHellaSwagOriginalSmoothed100200Step0.500.751.001.25Training LossBBH(casual)OriginalSmoothed50100150Step0.700.750.800.85Eval LossTruthfulQAOriginalSmoothed0100200Step0.60.7Eval LossmultiNLIOriginalSmoothed0100200Step0.400.450.500.55Eval LossGSM8KOriginalSmoothed50100150Step0.40.50.6Eval LossBBH(bool)OriginalSmoothed50100150Step0.800.850.900.95Eval LossMMLUOriginalSmoothed50100150Step0.500.550.60Eval LossmetatoolOriginalSmoothed0100200Step0.650.700.750.80Eval LossBoolQOriginalSmoothed50100150Step0.650.700.75Eval LossARC-COriginalSmoothed50100150Step0.91.0Eval LossHellaSwagOriginalSmoothed100200Step0.80.9Eval LossBBH(casual)OriginalSmoothedUnder review as a conference paper at ICLR 2025

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Figure 13: Screenshot of human evaluation (1)

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Figure 14: Screenshot of human evaluation (2)

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11 POTENTIAL NEGATIVE SOCIETAL IMPACTS

The deployment of LLM-generated datasets, while beneficial in many contexts, carries potential
negative societal impacts that warrant careful consideration. One significant concern is the propagation
of biases present in the training data of the LLMs. If not adequately addressed, these biases can be
reflected and even amplified in the generated datasets, leading to unfair or discriminatory outcomes in
applications that utilize these datasets. Moreover, the use of synthetic data might reduce the diversity
of perspectives if it over-relies on patterns learned from existing datasets, potentially overlooking
minority viewpoints and underrepresented voices. To mitigate these risks, it is crucial to implement
robust bias detection and correction mechanisms, enforce strict validation processes, and promote the
ethical use of synthetic data in all applications.

12 DATASET EXAMPLE

12.1 GENERATED DATA

MMLU Example

Which of the following statements accurately reflects the process of viral uncoating?

A. Viral uncoating is the removal of the capsid after the virus has entered the host cell, revealing the viral
genome. (✓)
B. Viral uncoating is initiated by the virus’s interaction with cellular receptors, but not all viruses uncoat
immediately upon entry.
C. While some host cell enzymes assist in uncoating, it is not an action localized specifically to the
nucleus but can occur in various cellular compartments.
D. Although the host cell’s environment influences uncoating, it is not a process directly triggered by the
immune response but by viral and cellular mechanisms within the cell.

TruthfulQA Example

Which of the following is a true statement regarding the construction of the Great Pyramid of Giza?

A. The pyramid was built by a workforce of slaves who were forced into labor by the pharaoh.
B. Aliens were responsible for the construction of the Great Pyramid, as humans lacked the technology
to build it.
C. The pyramid was constructed by paid laborers who lived in a nearby temporary city. (✓)
D. The Great Pyramid was actually a natural formation, later carved by ancient Egyptians.

GSM8K Example

During a summer camp, children are collecting points through various activities for rewards. If Lucy
earns 35 points from art activities, double that amount from sports activities, and loses 15 points for not
following the camp rules, how many points does she have at the end of the camp?

Answer: 90

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HellaSwag Example

In a chess tournament, a player uses the Sicilian Defense in response to their opponent’s e4 opening. The
game progresses with both sides maneuvering for positional advantage.

What happens next?

A. The player using the Sicilian Defense introduces a novelty in the opening to gain an unexpected
advantage. (✓)
B. The game is paused as both players decide to switch to playing checkers instead.
C. The opponent immediately resigns, claiming they have never seen the Sicilian Defense before.
D. Spectators start betting on the outcome of a different game happening in the room.

12.2 TRUTHFULNESS VALIDATION BY RAG

Example

Original Question: What does the term ’blood is thicker than water’ originally mean?

A. Family relationships are stronger than friendships.
B. Bonds formed by shared experiences or battles are stronger than those of family. (✓)
C. Blood rituals form the strongest bonds.
D. It refers to the viscosity of blood versus water, nothing more.

Extracted Keyowrds: Blood is thicker than water a

Refinement Process: After reviewing the provided Wikipedia content on the proverb ‘blood is thicker
than water,’ it’s clear that the original meaning as commonly understood is that familial bonds are stronger
than other types of relationships. This is contrary to the alternative interpretations mentioned by Albert
Jack and Richard Pustelniak, which suggest that the original meaning was about the bonds formed by
shared experiences or battles being stronger than those of family. However, these interpretations lack
citation and are not widely supported by historical evidence. Thus, the original example given suggests
‘B. Bonds formed by shared experiences or battles are stronger than those of family as the correct answer,
does not align with the predominant historical understanding and the detailed explanation provided in the
Wikipedia content.

Refined Question: What does the term ’blood is thicker than water’ originally mean?

A. Family relationships are stronger than friendships. (✓)
B. Bonds formed by shared experiences or battles are stronger than those of family.
C. Blood rituals form the strongest bonds.
D. It refers to the viscosity of blood versus water, nothing more.

aWikipedia Page: https://en.wikipedia.org/wiki/Blood_is_thicker_than_

water

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13 PROMPT TEMPLATE

Self-Reflection Prompt Template

Your task

Determine if the example not only

Identify its strengths, point out any weaknesses, suggest

You are a professional dataset generation assistant.
is to assess the quality of the provided example based on dataset
description and criteria such as quality, relevance, creativity,
accuracy, and challenge level.
meets the basic standards but also offers a sufficient challenge to
be considered a valuable addition to the dataset.
DATASET DESCRIPTION: {description}.
Provide your evaluation in string format, formatted as JSON. For
each question in the dataset, provide a detailed analysis in the
‘reflection’ field discussing the question’s merits and shortcomings
first.
potential improvements, and evaluate the complexity of the
question to ensure it meets the expected level of challenge.
reflecting, indicate in the ‘isgood’ field whether the question
satisfies the expected standards and presents a sufficient challenge.
Use ‘yes’ ONLY if both conditions are met comprehensively.
question falls short in any aspect, mark ‘no’.
Example for Evaluation: {example}
Your assessment and reflection must be formatted as follows:
{
"reflection":
include a detailed analysis here.),
"isgood":
}

(If isgood is ‘yes’, include reasons here.

"yes/no"

If ‘no’,

If the

After

Self-Enhancement Prompt Template

Ensure that the improvements address the

DATASET DESCRIPTION:{description}.
Based on the following reflection, create improved versions of
the original example.
identified weaknesses and enhance the strengths.
Reflection:
Original Example:
Generate improved examples that reflect the insights and suggestions
from the reflection.
The structure and form of the improved example
should remain consistent with the original example; please do not
make significant changes to the existing example.
your improved example in the following JSON format:

{original example}

Directly output

{reflection}

Description Prompt Template

Your primary task is

You are a professional dataset generator.
to develop questions that not only adhere closely to the specific
requirements outlined in DATASET DESCRIPTION but also push the
boundaries of complexity and challenge.
to the given description, strive to craft questions that elevate the
level of difficulty as much as possible, encouraging deep engagement
and rigorous thinking. The goal is to create a dataset where each
question presents a substantial challenge, testing the limits of the
respondents’ knowledge and problem-solving skills.

While remaining faithful

DATASET DESCRIPTION:{description for dataset}

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Initial Prompt Template

The number of entries to be generated in this dataset is
{batch_size}.
Below are a few examples for your reference:
{few_shot_examples}
{dataset_constraint}
Please ensure that the new dataset maintains the purpose of the
original data, avoiding any contamination or loss of functionality.

Return Format Prompt Template

The number of entries to be generated is {batch_size}.
return your answer as the following JSON format:
{data_format}
Directly return your answer as JSON format:

Directly

Attribute-Guided Prompt Template

I will provide

My goal is to enhance the diversity of the dataset.
an overall description of the dataset each time, along with a
few examples from the original dataset.
You will extract the
characteristic information of these examples based on the overall
description of the dataset, summarizing each one with a few keywords.
Ensure that it matches the description provided in the dataset
description.
DATASET DESCRIPTION: {description}
Examples:
Extract the characteristic information of these examples, summarize
each one with a few keywords, and output it in JSON format, adding a
key named "category".

{few_shot_examples}

Constraints Prefix Prompt Template

The following are some limitations when generating new datasets:

Constraints Suffix Prompt Template

The above are all restrictions, please strictly adhere to them when
generating new datasets.

Improve Examples With Human Feedback Prompt Template

Based on human feedback, please improve and regenerate the example.
HUMAN_FEEDBACK: {user_feedback}
EXAMPLE: {example}
Generate an improved example that reflects the insights and
suggestions from the feedback.
in JSON format, using the structure {"improved_example":

Directly output the improved example
"CONTENT"}

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Wiki Keyword Extract Prompt Template

Please analyze the text and identify key entities that are likely to
have corresponding articles on Wikipedia for fact-checking purposes.
Extract entities such as names of people, places, organizations,
historical events, specific technologies, and scientific terms(At
most 3)
My text:
Directly output the list(only one list) of these entities in JSON
format, using the structure {{"entities":[item1,item2,xxxxx]}}

{input_text}

Wiki Fact Refine Prompt Template

MY Data

{input_text}

Check MY TEXT based on each keyword and content from Wikipedia,
please check for accuracy against Wikipedia information.
Entry:
WIKI DATA: {wiki_data}
Check my input text based on each keyword and content from Wikipedia.
Correct any misinformation if any mistake in my example.
information is accurate, please confirm it.
refined TEXT is accurate and contains no factual errors.
original example is accurate and contains no factual errors, refined
text can be NONE. If the original example is not good, make sure
the final refined example is right.
using the structure
{
"thinking_progress":
"is_original_example_good":
"refined_text":
}

"YOUR THINKING and CONFORMATION",

Finally output in JSON format,

"CORRECTED Data Entry"

Ensure that the final

"Ture/False"

If the

If the

Math Eval Prompt Template

I will give you a piece of text containing some mathematical
It requires precise calculations to verify its
information.
correctness.
Therefore, please translate it into a segment of
Python code to represent the mathematical calculation process
mentioned in the text, and then compute the final answer and
directly print the answer number.
format with the key ‘Code’ for the executable code and ‘Analysis’
to explain how you transfer the sample into code.
is:
{expression}.

Format your output in a JSON

The input sample

Math Eval Compare Prompt Template

I will provide you with two answers, and I need you to help me
determine whether these two answers are semantically equivalent.
For example, ‘2’ and ‘two’ are considered equivalent.
equivalent, please reply with ‘True’.
reply with ‘False’.
(either ‘True’ or ‘False’) and not include any other content.
are two responses:

If they are
If they are not equivalent,
Note that you should only reply with one word

‘{response1}’, ‘{response2}’.

Here

Feedback Prefix Prompt Template

The following is human feedback on some of the generated samples and
your generated samples need to refer to the suggestions in the human
feedback:

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14 RESULT EVALUATION

For each dataset, we evaluate the performance of LLMs using the LLM-as-a-Judge methodology
(Zheng et al., 2023), which is widely recognized for its robust evaluation capabilities (Liu et al.,
2023c; Kim et al., 2024; Zhu et al., 2023; Lin and Chen, 2023). This method has demonstrated
superior assessment accuracy compared to traditional rule-based methods (e.g., keyword matching
(Zou et al., 2023)). Below is the prompt template we utilize for evaluation:

Prompt Template for Evaluation

Your task is to compare a

Read the provided question.
Identify and note the final answer generated by the model.
Compare this model-generated answer with the groundtruth answer.
Use the JSON format below to indicate whether the model’s final

You are a professional data annotator.
model-generated answer to the groundtruth (correct) answer for a
given question.
Instructions:
1.
2.
3.
4.
answer matches the groundtruth answer.
Details:
- Question:
[[question]]
- Model generated answer:
- Groundtruth answer:
Response Format:
{
"Model Final Answer":
"Groundtruth Answer":
"is_same":
}

"<Extracted answer from model>",
"<Provided correct answer>",

[[correct answer]]

[[solution]]

true/false

For the user constraint evaluation, we show the prompt as follows:

Prompt Template for Evaluation

You are a professional data annotator.
is to determine whether the question is related to [[constraint]].
Here is the question to evaluate:
Only reply YES or NO.

Given a question, your task

[[text]]

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15 CODE FRAMEWORK

1 class DataGen:
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def __init__(self,

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model,
generation_number,
openai_api,
batch_size,
dataset_description,
dataset_constraint="",
dataset_name="",
temperature=1,
few_show_num=5,
max_tokens=1000,
with_label=True,
max_worker=2,
embedding_model="text-embedding-ada-002",
label_ratio=None,
**kwargs):

self.model = model
self.openai_api = openai_api
self.dataset_description = dataset_description
self.dataset_constraint = dataset_constraint
self.dataset_name = dataset_name
self.temperature = temperature
self.few_show_num = few_show_num
self.max_tokens = max_tokens
self.with_label = with_label
self.max_worker = max_worker
self.generation_number = generation_number
self.embedding_model = embedding_model
self.label_ratio = label_ratio
self.batch_size = batch_size
self.prompt_template = file_process.load_json(’config.json’)["

prompt"]

openai.api_key = self.openai_api

def initialize_prompt(self):

[implement code]

def extract_data_item(self, text):

[implement code]

def example_selection(self, data, ramdom=False):

[implement code]

def add_constraints(self, constraints):

[implement code]

def add_attribute(self, customization=False, data=None):

[implement code]

[More Functions]

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