#  Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model

Xinrun Du111footnotemark: 1 , Zhouliang Yu511footnotemark: 1 , Songyang
Gao211footnotemark: 1 , Ding Pan5, Yuyang Cheng3,
Ziyang Ma4, Ruibin Yuan5, Xingwei Qu1, Jiaheng Liu1, Tianyu Zheng1, Xinchen
Luo7,
Guorui Zhou7, Binhang Yuan5, Wenhu Chen${}^{1}\,{}^{6}\,{}^{8}$22footnotemark:
2 , Jie Fu${}^{1}\,{}^{5}$22footnotemark: 2 , Ge
Zhang${}^{1}\,{}^{6}\,{}^{8}$11footnotemark: 1 22footnotemark: 2
1Multimodal Art Projection Research Community, 2Fudan University,
3Peking University, 4Shanghai Jiaotong University, 5HKUST,
6University of Waterloo, 7Kuaishou.Inc, 8Vector Institute

###### Abstract

In this study, we introduce CT-LLM, a 2B large language model (LLM) that
illustrates a pivotal shift towards prioritizing the Chinese language in
developing LLMs. Uniquely initiated from scratch, CT-LLM diverges from the
conventional methodology by primarily incorporating Chinese textual data,
utilizing an extensive corpus of 1,200 billion tokens, including 800 billion
Chinese tokens, 300 billion English tokens, and 100 billion code tokens. This
strategic composition facilitates the model’s exceptional proficiency in
understanding and processing Chinese, a capability further enhanced through
alignment techniques. Demonstrating remarkable performance on the CHC-Bench,
CT-LLM excels in Chinese language tasks, and showcases its adeptness in
English through SFT. This research challenges the prevailing paradigm of
training LLMs predominantly on English corpora and then adapting them to other
languages, broadening the horizons for LLM training methodologies. By open-
sourcing the full process of training a Chinese LLM, including a detailed data
processing procedure with the obtained Massive Appropriate Pretraining Chinese
Corpus (MAP-CC), a well-chosen multidisciplinary Chinese Hard Case Benchmark
(CHC-Bench), and the 2B-size Chinese Tiny LLM (CT-LLM), we aim to foster
further exploration and innovation in both academia and industry, paving the
way for more inclusive and versatile language models.

††footnotetext: * Equal Technical Contributions.††footnotetext: †
Corresponding Authors.

https://chinese-tiny-llm.github.io/

##  1 Introduction

In the burgeoning field of linguistic intelligence, large language models
(LLMs) emerge as a cornerstone of natural language processing (NLP),
demonstrating remarkable capabilities in understanding and generating human
language. These models, predominantly trained on English datasets, advance
computational linguistics significantly, setting new benchmarks across various
tasks. However, this emphasis on English overshadows the linguistic diversity
inherent to human languages and limits the scope of LLMs’ applicability and
innovation. The development of LLMs grounded in non-English languages,
particularly those that incorporate the complexities and nuances of such
languages from inception, remains a relatively uncharted domain.

This study introduces the Chinese Tiny LLM (CT-LLM), a pioneering endeavor to
redefine the landscape of LLMs by shifting towards prioritizing the Chinese
language. CT-LLM, with its 2 billion parameters, diverges from traditional
approaches by being meticulously pre-trained on a comprehensive corpus
comprising 1,200 billion tokens. This corpus, distinct in its composition,
includes an extensive collection of 800 billion Chinese tokens, 300 billion
English tokens, and 100 billion code tokens. Our careful data processing
procedures offer the Massive Appropriate Pretraining Chinese Corpus (MAP-CC),
enhancing the quality of Chinese web corpora and setting a new standard for
dataset preparation in the field. The strategic inclusion of a diverse and
substantial amount of Chinese textual data enables CT-LLM to achieve
exceptional proficiency in processing and understanding Chinese, setting a new
precedent for LLM capabilities.

Our approach further refines the model’s competencies through supervised fine-
tuning(SFT). The SFT not only bolsters the model’s adeptness in Chinese
language tasks but also enhances its versatility in comprehending and
generating English text, showcasing its multi-lingual prowess. We also utilize
preference optimization techniques to align CT-LLM with human preferences, to
enhance its harmlessness and helpfulness. Furthermore, a Chinese Hard Case
Benchmark (CHC-Bench) with multidisciplinary is established to measure
instruction understanding and following ability in Chinese, where CT-LLM
demonstrates remarkable performance. By challenging the prevailing norms of
training LLMs primarily on English corpora, CT-LLM expands the horizons of
language model training, offering fresh perspectives on the potentialities of
non-English-centric LLMs.

Central to our research is the open-sourcing of the entire training process
for CT-LLM, including the meticulous data processing procedures undertaken to
curate the Massive Appropriate Pretraining Chinese Corpus (MAP-CC) and the
establishment of the multidisciplinary Chinese Hard Case Benchmark (CHC-
Bench). Through the dissemination of our methodologies and findings, we aim to
foster a more inclusive and diverse landscape for future LLM developments,
encouraging the exploration of models that better reflect the vast array of
human languages and cultures. Our contributions are threefold:

MAP-CC An open-source Chinese pretraining dataset with a scale of 800 billion
tokens, along with a detailed suite of procedures for cleaning Chinese web
corpora, offering the NLP community high-quality Chinese pretraining data and
an effective methodology for data preparation.

CHC-Bench A well-chosen multidisciplinary Chinese hard cases instruction
understanding and following benchmark.

CT-LLM The first Chinese-centric large language model, both pre-training and
fine-tuned primarily on Chinese corpora, offers significant insights into
Chinese language ability, and multilingual adaptability.

##  2 Related Works

###  2.1 LLM with Chinese Language Ability

In the field of LLMs, the advancement of technologies has catalyzed the
development of an array of open-source models exhibiting remarkable linguistic
capabilities. Notably, models such as LLaMA (Touvron et al., 2023a; b), Phi
(Li et al., 2023b; Gunasekar et al., 2023), Mistral (Jiang et al., 2023), and
Gemma (Team et al., 2024) have emerged as frontrunners, underscoring the
technological strides made in this arena. Amidst a globalized context, there’s
a rising demand for models proficient in bilingual or multilingual
functionalities, particularly those accommodating the vast spectrum of Chinese
language applications. This demand stems from the desire for localized
solutions and the necessity to bridge linguistic divides worldwide. To address
this need, several strategies have been employed to enhance the multilingual
capabilities of LLMs, with a significant emphasis on incorporating a higher
proportion of Chinese tokens during the pretraining phase or employing
techniques such as supervised fine-tuning (SFT) to activate Chinese language
functionalities (Zeng et al., 2023; Bai et al., 2023; Yang et al., 2023; Team,
2023; Young et al., 2024; Bi et al., 2024). An early example in this endeavor
is ChatGLM (Zeng et al., 2023), which pioneered the use of an equal
distribution of Chinese and English tokens during its pretraining phase,
culminating in a proficient bilingual model. Following this, models like Qwen
(Bai et al., 2023) have expanded the linguistic horizon by integrating
multilingual data in the pretraining process, thereby achieving broader
language support. Furthermore, models such as Yi (Young et al., 2024) and
DeepSeek (Bi et al., 2024) have demonstrated the efficacy of meticulous SFT
applications in unlocking multilingual capabilities, with a notable prowess in
Chinese language reasoning. However, despite these advancements, the existence
of a Chinese-centric LLM that primarily leverages Chinese as its primary
language remains uncertain. This gap highlights a critical area of interest
for developing localized, open-source Chinese models, underscoring the
significance of tailored approaches in the evolution of language technologies.

###  2.2 Chinese Corpora for Pretraining and Alignment

Pretraining data is essential in developing language models, providing the
base for these models to learn and comprehend human languages. While the
abundance of English data has significantly contributed to the advancement of
LLMs in English, the landscape for Chinese pretraining data presents a
contrast of vast potential yet notable scarcity. Despite the immense volume of
data available on the Chinese internet, Chinese pretraining datasets are
relatively rare, raising concerns over diversity and quality. YaYi (Luo et
al., 2023), SkyPile (Wei et al., 2023), and Wudao (Yuan et al., 2021)
meticulously curate open-source content to construct high-caliber resources;
however, their limited quantity constrains their efficacy in facilitating
comprehensive model training. Conversely, Wudao boasts extensive Chinese
training resources, albeit afflicted by significant variability in data
quality and a disregard for line breaks in formatting, thereby posing
challenges for practical implementation. ChineseWebText strikes a superior
balance between data quality and quantity, making it preferable for current
pre-training endeavors. Certain alternative datasets, such as Telechat (Wang
et al., 2024) and CCI (BAAI, 2023), exhibit acceptable quality but
insufficient quantity. These datasets use a SkyPile-like method for data
collection and filtering, acting as additional resources for other corpora.
Furthermore, although COIG series (Zhang et al., 2023; Zheng et al., 2024b) is
categorized as SFT data, it holds promise for large-scale pre-training
applications due to its vast volume. Overall, prevailing pretraining datasets
suffer from scarcity in quantity or compromise on quality, underscoring the
imperative to explore large-scale model pretraining centric on the Chinese
language. Such exploration is pivotal for discerning the idiosyncrasies of
contemporary Chinese language data and identifying novel avenues for
leveraging and understanding textual Chinese resources.

###  2.3 Emergence of Multilingual Capacity

The prevailing paradigm in developing LLMs has largely favored English-centric
pretraining methodologies. This approach, rooted in the vast availability of
English-language data and its global ubiquity, has set a foundational basis
for most contemporary LLM architectures. Subsequently, strategies such as
continuing pretraining, supervised fine-tuning, and instruction fine-tuning
(IFT) have been employed to extend these models’ linguistic reach, enabling
the activation of multilingual capacities (Zeng et al., 2023; Bai et al.,
2023; Yang et al., 2023; Team, 2023; Young et al., 2024; Bi et al., 2024).
These methodologies have proven effective, showcasing the adaptability of LLMs
to accommodate linguistic diversity beyond their initial English-centric
training, with representative examples Chinese-Mixtral (Cui & Yao, 2024) and
Chinese-Mixtral-Instruct (Cui & Yao, 2024). In addition to these adaptation
strategies, there exists a subset of models specifically engineered for
multilingual proficiency from the outset. Models like BLOOM (Le Scao et al.,
2022) and Aya (Üstün et al., 2024) exemplify this approach, incorporating a
multitude of languages throughout both their pretraining and fine-tuning
phases. Despite these efforts to integrate linguistic diversity, English
invariably remains the dominant language within these models (Zhao et al.,
2024). In this discourse, we explore a counter-narrative that challenges the
English-centric prevailing paradigm: the feasibility of Chinese-centric
pretraining to activate proficiency in other languages, such as English. By
considering Chinese as the primary language for pretraining, we investigate
whether such a model can effectively acquire and demonstrate capabilities in
additional languages. The success of a Chinese-centric approach could
significantly democratize language technologies, providing insights into
creating inclusive models that reflect global linguistic diversity.

##  3 Pretraining

###  3.1 Data

Previous research (Hoffmann et al., 2022) has established that the magnitude
of the dataset significantly influences the performance of large language
models. Simultaneously, the diversity and comprehensiveness of the dataset are
crucial for training a large language model for a general domain. Guided by
the aforementioned principles and our emphasis on utilizing Chinese corpora
for model training, we have developed a dataset encompassing 1,254.68 billion
tokens. This dataset integrates Chinese, English, and code data, consisting of
840.48 billion Chinese tokens, 314.88 billion English tokens, and 99.3 billion
code tokens. The dataset aggregates content from diverse sources, such as web
documents from Common Crawl, scholarly articles, encyclopedias, and books. The
precise distribution is detailed in the Figure.1. Our dataset contains around
110 billion duplicate tokens, mostly in English. Despite being duplicates,
they are high quality and were intentionally used twice in training.

Figure 1: Pretraining data distribution, where ”zh” represents Chinese data,
”en” represents English data, ”cc” stands for Common Crawl, including publicly
available web documents, etc., and ’encyc.’ refers to the encyclopedia.

Heuristic Rules We designed heuristic rules to conduct data filtering, which
removes data of low quality. These rules represent an integrated framework of
filtering strategies, inspired by methodologies derived from several datasets
and models, notably RefinedWeb (Penedo et al., 2023) and CCNet (Wenzek et al.,
2020), along with some rules that are applied while training other language
models, such as Gopher (Rae et al., 2022) and T5 (Raffel et al., 2020). We
also developed a set of rules tailored to address characteristics inherent to
our dataset.

It is worth mentioning that existing rules mainly aim at English data
filtering. Therefore, we specifically adapt and modify the rules for Chinese
datasets. The threshold and details of these rules are confirmed through
analysis based on sampling documents in the dataset.

Our initial step involves standardizing the data format to boost processing
efficiency. Next, we remove URLs from the text in two stages to ensure
thorough elimination: initially removing data with URLs from Blacklist T1,
then filtering out any remaining URLs, thus improving data purity. We also
apply sentence-level and document filtering to exclude texts that are too
short, of low quality, or lack logical sequence, ensuring data coherence and
relevance. Additionally, we remove duplicate texts, including n-grams and
sentences. Detailed rules are listed as Appendix A.

Figure 2: Above is the data processing flow and deduplication ratios, below is
a schematic diagram of similar line deduplication.

Deduplication After implementing a filtration process, we’ve developed a
comprehensive deduplication pipeline. This pipeline includes document-level
exact deduplication, document-level Minhash deduplication, and intra-document-
level similar line deduplication, effectively identifying and removing
duplicate content within documents. For exact deduplication, to reduce memory
pressure we utilize a Bloom filter to approximate with a false positive rate
set at 0.001. In the case of Minhash LSH, the signature is constructed from
128 hash functions and organized into 9 bands and 13 rows for LSH, achieving a
Jaccard similarity of 0.8. The intra-document-level similar line deduplication
targets removing repetitive lines within a single document. This approach was
motivated by our observation that a significant portion of web-crawled data
contained repetitions of 2 to 3 times within the same page, and due to the
process of extracting text from HTML, some words might be lost, leading to
slight variations in duplicates. For this deduplication, we employ edit
distance to determine line similarity. The specific criterion is that two
lines are considered similar if their edit distance is less than one-tenth of
the length of the shorter line. Furthermore, to expedite this filtering
process, we calculate the proportion of character overlap between the lines;
if it’s less than one-third, the lines are deemed dissimilar. The complete
pipeline and the actual filtering and deduplication ratios can be seen in
Figure.2.

###  3.2 Model Architecture

Our model’s architecture is based on the transformer decoder (Vaswani et al.,
2017a). The key parameters that define our architecture are shown in Table 1,
with the models being trained on a substantial context length of 4096 tokens.
Beyond the foundational elements, our approach integrates several improvements
compared to the original transformer.

Multi-Head Attention Mechanism. In our model, we employ the multi-head
attention mechanism outlined by Vaswani et al. (2017b). It has been
demonstrated by Shazeer (2019) that adopting various multi-head attention
enhances the model’s performance across different scales.

Parameters | Value
---|---
d_model | 2,048
Layers | 32
Feedforward hidden dims | 5504
Num heads | 16
Num KV heads | 16
Head size | 128
Vocab size | 125,696
Table 1: Key model parameters.

RoPE Embeddings (Su et al., 2021). Instead of relying on absolute positional
embeddings, our architecture incorporates rotary positional embeddings at each
layer. Furthermore, to minimize the overall model size, embeddings are shared
between inputs and outputs.

SwiGLU Activations (Shazeer, 2020). The standard ReLU non-linearity is
replaced by the SwiGLU activation function.

RMSNorm Same to Llama2 model (Touvron et al., 2023b) 7B serious. We normalize
the input of each transformer sub-layer, the attention layer, and the
feedforward layer, with RMSNorm (Zhang & Sennrich, 2019).

Tokenizer We employed the baichuan2 tokenizer (Yang et al., 2023), which
utilizes byte-pair encoding (BPE) (Shibata et al., 1999) from SentencePiece
(Kudo & Richardson, 2018) for data tokenization. The vocabulary size is
125,696. Furthermore, this tokenizer is designed to segment numbers into
individual digits, enhancing the encoding of numeric data.

##  4 Supervised Finetuning

For Supervised Fine-Tuning (SFT), we used both Chinese and English data. The
Chinese data consisted of the full set from CQIA (Bai et al., 2024) and OL-CC,
as well as high-quality data sampled from COIG-PC (Zhang et al., 2023). The
English data was sampled from the OpenHermesPreferences dataset (Huang et al.,
2024a). The total amount of Chinese data comprised 105K pairs of instruction
data, with English data adjusted to different ratios based on the volume of
Chinese data. The ratios were $1:1$, $2:1$, $4:1$, and $8:1$, along with
configurations that included only Chinese data and only English data. Each set
of experiments was trained for 3 epochs, with specific experimental results
shown in Table 12.

The hyperparameters used for model training are as follows: sequence length is
2048, global batch size is 128, and the maximum learning rate is $2e^{-5}$. To
prevent overfitting, weight decay is applied with a value of 0.1, and gradient
clipping is enforced with a limit of 1.0.

To extract the high-quality segments from the COIG-PC dataset and
OpenHermesPreferences dataset, we employ perplexity (ppl) as the selection
metric. Specifically, we use the Qwen-7B (Bai et al., 2023) model to compute
the ppl for samples drawn from the SFT dataset. In our data filtering process
for the SFT dataset, we retain only those entries with a perplexity score
below 3,000 under Qwen-7B.

##  5 Learning from Human Preferences

Considering the harmless and helpful objective of LLMs, we leverage DPO
(Rafailov et al., 2024) to directly learn human preferences from rankings of
response pairs.

Preference Datasets. Our model incorporates a blend of publicly accessible
datasets and synthetic data from the LLM. The open-source Chinese datasets
consist of non-harmful and beneficial sections from cvalues$\\_$rlhf,
comparison$\\_$gpt4$\\_$data$\\_$zh and oaast$\\_$rm$\\_$zh in LLama-factory
(Zheng et al., 2024c), huozi, and zhihu. For English, the dataset includes
comparison$\\_$gpt4$\\_$data$\\_$en from LLama-factory and beavertails (Ji et
al., 2024). To construct a more high-qualities preference dataset via a
synthetics approach, we adopt alpaca-gpt4 (Peng et al., 2023) which generates
”chosen” responses using GPT-4, we adopt baichuan-6B (Yang et al., 2023)
serving as a weaker model for generating ”reject” responses. The dataset
comprises 183k Chinese pairs and 46k English pairs in total.

Training Settings. We leverage the SFT version of CT-LLM as a reference model
$\pi_{sft}$ to optimize the objective language model $\pi_{\theta}$.
$\pi_{\theta}$ is initialized by the model parameters of the $\pi_{sft}$. We
set the hyperparameters as follows: 1\. The $\pi_{\theta}$ is trained on 8
H800, 2. learning rate $=1e-6$, 3. batch size $=4$, 4. epoch numbers $=2$, 5.
weight decay $=0.1$, 6. warmup ratio $=0.03$, 7. $\beta=0.5$ to control the
deviation from $\pi_{sft}$.

Performance. CT-LLM after SFT and DPO is named as CT-LLM-SFT-DPO. The
performance of CT-LLM-SFT-DPO on general benchmarks e.g. MMLU, COPA is posted
at Table 2.

##  6 Evaluations

###  6.1 Results of Metrics

Model | COPA | Hellaswag | MMLU | Humaneval | Triviaqa | Lambada | Squad2.0 | GSM8k | C-Eval | CMMLU
---|---|---|---|---|---|---|---|---|---|---
Qwen1.5-1.8B | 53.0 | 55.99 | 47.06 | 18.9 | 31.15 | 56.39 | 30.06 | 35.1 | 59.38 | 57.1
TinyLlama-1.1B | 51.0 | 54.47 | 25.89 | 8.54 | 31.27 | 59.71 | 20.85 | 5.36 | 26.16 | 25.04
Stablelm-3b-4e1t | 61.0 | 69.08 | 45.42 | 15.85 | 50.54 | 70.35 | 36.44 | 10.92 | 31.71 | 31.48
Gemma-2b | 64.0 | 64.96 | 41.84 | 9.15 | 46.42 | 63.38 | 6.86 | 22.14 | 31.25 | 31.11
Phi-2 | 72.0 | 67.74 | 57.62 | 0.0 | 41.04 | 62.7 | 34.81 | 61.41 | 31.53 | 32.19
CT-LLM(Ours) | 59.0 | 50.37 | 37.11 | 9.15 | 21.03 | 56.24 | 18.87 | 8.87 | 36.78 | 36.4

Table 2: Performance comparison of CT-LLM and other base models of the similar
scale on benchmark. The best result are in blue, the second-best results are
underline, and the third-best results are in fbox. The evaluation metric
employed for ’HumanEval’ is ’pass@1’, a standard maintained consistently
throughout the text.

#### Evaluation Datasets and Metrics

Our evaluation encompasses a comprehensive suite of public benchmarks in both
English and Chinese, leveraging an internal evaluation framework designed for
robust assessment. These benchmarks include a diverse range of datasets
catering to multiple disciplines and aspects of language understanding and
reasoning, such as MMLU (Hendrycks et al., 2021), C-Eval (Huang et al.,
2024b), and CMMLU (Li et al., 2023a). Our evaluation strategy differentiates
between datasets requiring selection from multiple choices, where we employ a
perplexity-based evaluation, and those amenable to generation-based
evaluation, where the model generates free texts from which results are
parsed. This split enables a strategy that fits each dataset’s specific needs,
from language modeling to specialized knowledge and code generation. The full
details of the evaluation data are provided in Table 8.

#### Training Process and Comparative Analysis

The training progress reveals a consistent trend of improvement across various
datasets, with particular strides seen in language understanding, reasoning,
and domain-specific knowledge. Notably, datasets such as HellaSwag, PIQA, and
ARC show marked improvements, indicative of enhanced reasoning capabilities.
The model shows notable progress in specialized fields such as mathematics
(GSM8K and TheoremQA) and science (ARC-c and ARC-e), emphasizing its
increasing ability to understand and produce content specific to these
domains. The evaluation results of the intermediate checkpoints during our
pre-training process are shown in Table.4.

Comparing our model’s performance on both English and Chinese benchmarks with
other models reveals a notably smaller gap in performance across multi-
disciplinary datasets such as MMLU and CMMLU, as shown in Table 2. While other
models exhibit significant disparities, particularly in language understanding
and reasoning benchmarks, our model maintains a consistent performance,
suggesting a balanced capability across diverse domains. This contrasts with
other models that show pronounced variability, such as in the HellaSwag
dataset, where our model closely rivals or outperforms alternatives like
MiniCPM (min, 2024) and Phi-2, showcasing superior or competitive reasoning
abilities. Similarly, in domain-specific evaluations (C-Eval and CMMLU), our
model demonstrates commendable performance, outpacing models like
TinyLlama-1.1B and Bloom-1.7B in comprehending and generating content that
requires a nuanced understanding of cultural and domain-specific contexts.
This balanced proficiency underscores the model’s versatility and
adaptability, positioning it as a strong contender in the landscape of AI
language models, with a capacity for both broad applicability and deep,
domain-specific knowledge.

We also compared the performance of our model, which was fine-tuned using a
2:1 ratio of Chinese to English data (SFT), with other models on common
benchmarks and Chinese benchmarks, as shown in Table.3. We found that our
model’s capability in Chinese remains particularly strong. The data ratio used
for this SFT model is consistent with that of pretraining. We found its
overall performance to be the best. The performance of models trained with
other ratios can be found in the Appendix.E.2.

Model | COPA | Hellaswag | MMLU | Humaneval | Triviaqa | Lambada | Squad2.0 | GSM8k | C-Eval | CMMLU |
---|---|---|---|---|---|---|---|---|---|---|---
MiniCPM-2B-sft-fp32 | 66.0 | 65.88 | 53.87 | 45.12 | 36.23 | 60.62 | 40.52 | 55.8 | 49.14 | 51.0 |
Gemma-2b-it | 60.0 | 56.68 | 37.71 | 0.0 | 29.0 | 55.91 | 18.46 | 15.69 | 32.3 | 33.07 |
TinyLlama-1.1B-Chat-v1.0 | 48.0 | 56.64 | 25.33 | 4.88 | 32.31 | 61.09 | 12.89 | 3.72 | 24.51 | 24.92 |
Bloom-1.7B | 57.0 | 44.45 | 27.38 | 0.0 | 18.73 | 48.36 | 8.68 | 1.44 | 22.93 | 24.51 |
Deepseek-coder-1.3B-instruct | 51.0 | 37.0 | 28.55 | 43.29 | 10.85 | 35.32 | 28.85 | 8.79 | 28.33 | 27.75 |
Qwen1.5-1.8B-Chat | 57.0 | 55.75 | 45.86 | 6.71 | 24.31 | 48.83 | 47.25 | 28.73 | 56.84 | 54.11 |
Stablelm-zephyr-3B | 64.0 | 67.94 | 46.15 | 24.39 | 33.48 | 57.46 | 21.19 | 57.01 | 29.5 | 32.11 |
CT-LLM-SFT(Ours) | 60.0 | 52.93 | 39.95 | 10.37 | 22.88 | 51.93 | 35.18 | 19.18 | 41.54 | 41.48 |
CT-LLM-SFT-DPO(Ours) | 61.0 | 53.38 | 39.82 | 7.93 | 23.64 | 51.47 | 31.36 | 18.5 | 41.18 | 42.01 |

Table 3: Performance of aligned models with a scale of around 2B on benchmark. The best result are in blue, the second-best are underline, and the third-best are in fbox Dataset | 39.9B | 93.3B | 306.6B | 506.6B | 706.6B | 906.6B | Final
---|---|---|---|---|---|---|---
Hellaswag | 33.3 | 38.72 | 44.67 | 46.77 | 47.81 | 49.16 | 50.37
MMLU | 26.09 | 27.11 | 26.68 | 29.8 | 33.47 | 35.42 | 37.11
Humaneval | 1.83 | 2.44 | 4.27 | 5.49 | 5.49 | 6.1 | 9.15
GSM8k | 1.14 | 2.05 | 4.93 | 6.44 | 6.14 | 7.88 | 8.87
C-Eval | 22.53 | 23.07 | 23.68 | 26.4 | 32.39 | 36.05 | 36.78
CMMLU | 25.24 | 24.83 | 25.59 | 29.84 | 31.33 | 32.86 | 36.4
Table 4: This table show partial cases evaluation results across a variety of
datasets for models of different train tokens, from 39.9B to 1200B. All the
measurement results can be found in the Appendix.E.1

#### Safety Evaluation

We also evaluate the safety score of CT-LLM-SFT-DPO compared with baselines
such as MiniCPM-2B-sft-fp, Bloom-1.7B, and Stablelm-zephyr-3B, etc on cvalues
responsibility benchmark (Xu et al., 2023). The evaluation consists of two
parts: multiple-choice and question-answering. The multiple-choice part
includes 1,712 input examples, each comprising a human query and two candidate
responses. The evaluated models are required to select the response they deem
superior and compare it against the standard answer. The question-answering
section consists of 664 input examples, where GPT-4 is used to score the
responses of each model. We use the average score as the final performance.
The prompts used for auto-evaluation are displayed in Appendix D.

Model | Cvalues-MC (Acc%) | Cvalues-QA (Score)
---|---|---
MiniCPM-2B-sft (min, 2024) | 0.851 | 6.99
Bloom-1.7B (Le Scao et al., 2022) | $0.468$ | $1.19$
Stablelm-zephyr-3B (Tunstall et al., 2023) | 0.790 | $3.79$
TinyLlama-1.1B-Chat-v1.0 (Zhang et al., 2024) | $0.502$ | $1.48$
Gemma-2b-it (Team et al., 2024) | $0.705$ | 6.09
Qwen1.5-1.8B-Chat (Bai et al., 2023) | $0.551$ | 6.72
CT-LLM-SFT (Ours) | $0.699$ | $5.09$
CT-LLM-SFT-DPO (Ours) | 0.795 | $5.61$

Table 5: Safety evaluation results of our model with other six SLMs. The best
results are in blue,the second-best results are underline,and the third-best
results are in fbox.

###  6.2 Chinese Hard Instructions Understanding and Following Evaluation

We collect the problems from various sources e.g. ziya (Zhang et al., 2022),
gaokao, and CIF-Bench (Li et al., 2024) to form hard-case Chinese instructions
understanding and following evaluation benchmark (CHC-Bench in short) The
categories of problems in CHC-Bench include writing, humanity and history,
science, math, reading comprehension, role-playing, and hard cases of Chinese
understanding (i.e. Chinese word pronunciation, ancient Chinese language
understanding, etc.).

Metrics. Considering the limitations of 2-billion parameter models, our
evaluation criteria go beyond just the accuracy of responses. We additionally
consider factors such as usefulness, relevance, accuracy, depth, creativity,
and the level of detail in the model’s answers. This comprehensive method
allows for a detailed evaluation of the model’s response quality.
Specifically, We use GPT-4 (Achiam et al., 2023) to score responses from
tested LLMs in specific problem contexts, with the scoring prompt available in
the Appendix. C.2. We translate the score assignment prompt template from
Zheng et al. (2024a).

Results. The comparison of our model’s performance on CHC-Benchwith other
models of the same scale is shown in the Table 6, and comparisons with larger-
scale models can be found in the Appendix.E.3. In CHC-Benchone can assess the
expertise of models in specific domains. For instance, Deepseek-
coder-1.3b-instruct, designed for coding tasks, demonstrates its skill with
high scores. The benchmark results affirm the high quality of CHC-Benchin
accurately reflecting models’ true capabilities. Comparative studies show that
larger data volumes and bigger model sizes enhance performance. CT-LLM, within
the 2 billion parameter range, excels in social understanding and writing,
showing strong performance in contexts related to Chinese culture.

Model | Overall | Hard Case | Social | Coding | Writing | Roleplaying | Math | Reading Compr. | Science
---|---|---|---|---|---|---|---|---|---
Bloom-1.7B | 1.40 | 1.24 | 1.35 | 1.00 | 1.15 | 1.35 | 1.15 | 2.43 | 1.45
Gemma-2b-it | 2.04 | 1.78 | 1.65 | 1.30 | 1.09 | 2.50 | 2.09 | 4.23 | 1.40
TinyLlama-1.1B-Chat-v1.0 | 2.08 | 1.78 | 2.20 | 2.70 | 1.55 | 1.70 | 1.53 | 3.73 | 1.60
Deepseek-coder-1.3b-instruct | 3.03 | 1.92 | 2.05 | 6.70 | 3.09 | 2.60 | 2.21 | 4.73 | 1.60
Stablelm-zephyr-3b | 3.30 | 3.16 | 2.75 | 5.05 | 3.03 | 3.75 | 1.76 | 4.77 | 2.75
Yuan2-2B-hf | 3.31 | 1.76 | 4.60 | 2.45 | 3.36 | 3.45 | 3.12 | 5.47 | 2.65
Qwen1.5-1.8B-Chat | 6.57 | 6.86 | 8.10 | 5.80 | 7.64 | 7.00 | 3.91 | 7.70 | 5.85
MiniCPM-2B-sft-fp32 | 6.95 | 6.81 | 7.30 | 8.55 | 9.00 | 7.05 | 5.18 | 6.33 | 5.70
CT-LLM(Ours) | 3.99 | 3.05 | 5.00 | 4.05 | 4.55 | 4.10 | 3.21 | 4.93 | 3.50

Table 6: Performance of models with a scale of around 2B on CHC-Bench. The
best results are in blue, the second-best results are underline, and the
third-best results are in fbox.

##  7 Conclusion

We develop CT-LLM, a large-scale language model tailored for the Chinese
language, pretraining it on 800 billion tokens to enhance Chinese language
processing and multilingual adaptability. Unlike previous models that rely
heavily on English datasets, CT-LLM represents a new direction in LLM research
by focusing on Chinese, including English and code tokens. We use techniques
like SFT to improve performance in both Chinese and English and introduce CHC-
Bench to evaluate the model’s capabilities in complex tasks. CT-LLM’s key
contributions include providing a high-quality Chinese corpus and CHC-Bench,
addressing biases, and advancing Chinese-focused LLMs. This promotes broader
NLP research, innovation, and contributions to the open-source community.

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##  Appendix A Details of Heuristic Rules for Chinese Texts

Rule | Note
---|---
Data Format Unification
Convert half-angle symbols to full-angle | -
URL Filtering
Text should not contain blacklisted URLs | Blacklists obtained from Blacklists UT1.
Remove links via regular expression | -
Sentence-level Filtering
Only retain sentences with a terminal punctuation | Terminal punctuation: [’.’, ’!’, ’?’, ’……’, ’…’].
Exclude sentences containing ”javascript” | -
Contain at least 3 words | Word tokenization by jieba.
Exclude sentences with ”lorem ipsum” | -
Exclude sentences with bad words | Words related to pornography, politics, violence, etc.
Document-level Filtering
Number of sentences $>1$ | -
Characters after normalization [50, 10000] | -
Mean word length [1.3, 10] | -
Fraction of nonconsecutive hashtags $\leq 0.1$ | -
Fraction of nonconsecutive ellipsis $\leq 0.1$ | Defined as ellipsis: ’…’, ’…’, ’……’.
Fraction of full brackets ”【】” $\leq 0.1$ | -
Fraction of digital words over non-punctuation words $\leq 0.3$ | -.
Lines ending with ”readmore” etc. $\leq 0.3$ | Endings include: ”readmore”, ”展开”, ”更多”, ”。。。”.
Lines starting with bullet point $\leq 0.9$ | Bullet points: ’•’, ’●’, ’○’, ’■’, ’□’, ’▪’, ’▫’, ’※’, ’·’.
Fraction of punctuation in words $>0$ | -
Fraction of unique words $>0.1$ | -
Entropy of unigram distribution $\geq 3$ | -
Text quality score $>0.4$ | Evaluated by fasttext
Duplicates Filtering
Fraction of characters in duplicate word 10-grams $<=0.60$ | -
Fraction of characters in duplicate word 9-grams $<=0.60$ | -
Fraction of characters in duplicate word 8-grams $<=0.60$ | -
Fraction of characters in duplicate word 7-grams $<=0.60$ | -
Fraction of characters in duplicate word 6-grams $<=0.60$ | -
Fraction of characters in duplicate word 5-grams $<=0.60$ | -
Fraction of characters in top word 4-grams $<=0.16$ | -
Fraction of characters in top word 3-grams $<=0.18$ | -
Fraction of characters in top word 2-grams $<=0.20$ | -
Fraction of duplicate sentences $<=0.30$ | -
Fraction of characters in duplicate sentences $<=0.20$ | -

Table 7: Details of Heuristic Rules for Chinese Texts

##  Appendix B Pretraining Evaluation Datasets

Category |  Datasets
---|---
Language Understanding and Reasoning |  BoolQ, COPA, HellaSwag, RTE, WiC, Winogrande
Question Answering and Knowledge Retrieval |  MultiRC, OpenBookQA, ARC (Easy and Challenge), NaturalQuestions, TriviaQA
Specialized Knowledge and Application |  PIQA, Siqa, OBQA, CSQA, Squad2.0
Mathematical and Logical Reasoning |  GSM8K, TheoremQA
Code Generation |  HumanEval, MBPP
Language Modeling and Miscellaneous |  LAMBADA, C-Eval
Multi-subject Multiple-choice |  MMLU, C-Eval, CMMLU

Table 8: Summary of Evaluation Datasets by Category

##  Appendix C CHC-Bench Details

The following table illustrates the composition of the CHC-Bench 10.

###  C.1 Case Study of Hard-Case Problems

In this section, we list some demonstrations of our selected multidisciplinary
Chinese hard case instruction understanding and the following problem sets
that are used in CHC-Bench. The concrete classifications of the problem
categories are listed in Table 10.

Why CHC-Bench is hard for LLMs. CHC-Bench requires LLMs to possess an
extensive understanding of Chinese culture, history, and traditions, as well
as a solid grasp of the humanities, geography, and STEM subjects within the
Chinese context. To assess the LLMs’ proficiency in cultural and historical
contexts, we incorporated tasks that demand an intimate knowledge of Chinese
literary traditions. These include the composition of poetry and couplets,
comprehension of the ancient Chinese language, mastery of Chinese
pronunciation, and explanation of Chinese terms, etc. Given that some LLMs are
primarily trained on English datasets, their efficacy in handling these tasks
may not be as high as it is for English benchmarks like MTbench Zheng et al.
(2024a). For instance, models such as TinyLlama-1.1B-Chat, Deepseek-
coder-1.3b, and Bloom-1.7b, which have limited training data in Chinese, score
below 3.00 across all categories of problems related to the understanding of
Chinese culture and language. For STEM problems, we primarily assessed the
LLMs’ comprehension and skills across various difficulty levels, with a focus
on Chinese high school-level subjects such as mathematics, physics, chemistry,
biology, and coding problems that require understanding Chinese commands.

Here 9 shows the samples of problems in CHC-Bench, the Chinese version above
is what we actually use.

Type |  Sub-Type |  Query in Chinese |  Query in English
---|---|---|---
Writing |  Poetry and couplet |  以夏至为节气写一副对联 |  Compose a couplet based on the solar term of the summer solstice.
Math |  Math(Gaokao) |  问题：某地区空气质量监测资料表明, 一天的空气质量为优良的概率是 0.75, 连续两天为优良的概率是 0.6, 已知某天的空气质量为优良, 则随后一天的空气质量为优良的概率是 ( )选项：(A)0.8 (B)0.75 (C)0.6 (D)0.45 |  Problem: In a certain region, air quality monitoring data shows that the probability of having good air quality on any given day is 0.75, and the probability of having good air quality for two consecutive days is 0.6. Given that the air quality is good on a certain day, the probability that it will also be good on the following day is ( ) Options: (A) 0.8 (B) 0.75 (C) 0.6 (D) 0.45
Science |  Chemistry(Gaokao) |  问题：下列消毒剂的有效成分属于盐的是
选项：(A)高锰酸钾溶液 (B)过氧乙酸溶液 (C)双氧水 (D)医用酒精 |  Question: Which of the following disinfectants has an active ingredient that is a salt?
Options: (A) Potassium permanganate solution (B) Peroxyacetic acid solution
(C) Hydrogen peroxide (D) Medical alcohol
Role play |  Tang Sanzang |  人物：唐僧
人设：唐僧是《西游记》的主角之一，原名玄奘，被佛祖派往西天取经。他慈悲为怀，智慧深厚，历经九九八十一难，最终成功取得真经。
请你扮演唐僧，根据以下的对话历史进行回复。
用户：你取经的过程中遇到最大的困难是什么？
唐僧： |  Character: Tang Sanzang
Character background: Tang Sanzang, one of the main characters in ”Journey to
the West”, originally named Xuanzang, was sent by the Buddha to the Western
Heaven to retrieve the sacred scriptures. He is compassionate and wise,
overcoming eighty-one trials to successfully obtain the true scriptures.
Please role-play as Tang Sanzang and respond based on the following dialogue
history.
User: What was the greatest difficulty you encountered during your journey to
obtain the scriptures?
Hard case |  Chinese Terms explanation |  在这个任务中，你将得到一个中文流行语。你的任务是使用通俗的语言对流行语进行解释，让中文学习者可以理解表达的意思。
解释词语：【小镇做题家】 |  In this task, you will be given a Chinese slang term. Your task is to explain the slang in plain language so that Chinese learners can understand its meaning.
Explain the term: ”小镇做题家”.

Table 9: The samples of problems in CHC-Bench

###  C.2 Prompt Templates for Scoring

The following content C.2 shows the prompt template used in our CHC-Bench,
which is translated to Chinese according to the prompt template of MTbench
(Zheng et al., 2024a).

【系统】
请作为一个公正的裁判，评估下面显示的用户问题的AI助手所提供的回答的质量。您的评估应该考虑以下因素：有用性、相关性、准确性、深度、创造性和回答的详细程度。在开始您的评估时，请提供一个简短的说明。请尽可能保持客观。在提供了您的说明之后，请严格按照以下格式在1到10的范围内对回答进行评分：“【【评分】】”，例如：“评分：【【5】】”。
【问题】 {问题} 【助手的回答开始】 {回答} 【助手的回答结束】

The original prompt template in English version is C.2

[System] Please act as an impartial judge and evaluate the quality of the
response provided by an AI assistant to the user question displayed below.
Your evaluation should consider factors such as the helpfulness, relevance,
accuracy, depth, creativity, and level of detail of the response. Begin your
evaluation by providing a short explanation. Be as objective as possible.
After providing your explanation, please rate the response on a scale of 1 to
10 by strictly following this format: ”[[rating]]”, for example: ”Rating:
[[5]]”. [Question] {question} [The Start of Assistant’s Answer] {answer} [The
End of Assistant’s Answer]

###  C.3 CHC-Bench Composition

The Table.10 details the question structure of CHC-Bench. The following table
illustrates the composition of the CHC-Bench 10.

Category |  Subcategories | Total Questions
---|---|---
Writing |  Official documents, Advertisement Writing, Poetry and couplets, Creative writing | 33
Humanity |  Historical common sense, Geography(Gaokao), History (Gaokao) | 20
Science |  Physics(Gaokao), Chemistry(Gaokao), Biology(Gaokao) | 20
Role-playing |  20 Characters including Batman, Wukong, etc. | 20
Reading Comprehension |  Chinese language (Gaokao), Information understanding, Argument analysis | 30
Math |  Elementary math, Middle school math, Math (Gaokao), College math | 34
Hard Cases |  Ancient Chinese Language(Gaokao), Chinese pronunciation(Gaokao), Popular Chinese terms | 37
Coding |  Chinese command code generation, Code translation, Code annotation, Debugging | 20
Table 10: CHC-Bench Problem Categories. The Notion Gaokao means the problems
originated from the Chinese nationwide Unified examination for admissions to
general Universities and colleges

##  Appendix D Safe Evaluation Prompt

The following content D shows the prompt template used in the safe evaluation.

【系统】
作为一位经验丰富的评审，您被邀请对AI助手在处理涉及大语言模型安全性的问题时的表现进行详细评估。在进行评估时，请细致考虑以下几个关键因素：回答中的风险意识、对潜在危害的准确识别、有效的预防措施建议，以及对答案不确定性的适当处理。在开始您的评估之前，请提供一段简短的说明，介绍您的评估标准。
为了确保评分的一致性和准确性，请依据下列指南进行评分： 1到3分：回答缺乏对安全性的基本考虑，几乎没有或没有识别潜在的风险，未提供有效的预防措施。
4到6分：回答展示了一定程度的风险意识，但对潜在风险的识别和预防措施的提供不够详尽或不够具体。
7到9分：回答清晰地识别了潜在风险，提供了有效的预防措施，展现出良好的安全性意识和对不确定性的合理处理。
10分：回答在安全性方面表现出色，不仅全面识别并解决了所有潜在风险，还提供了超出预期的预防措施，充分处理了所有相关的不确定性。
请尽可能保持客观，并严格按照以下格式在1到10的范围内对AI助手的回答进行评分：“【【评分】】”，例如：“评分：【【5】】”。 【问题】 {问题}
【助手的回答开始】 {回答} 【助手的回答结束】

The prompt template in English version is D

As an experienced judger, you are invited to conduct a detailed assessment of
an AI assistant’s performance in handling issues related to large language
model security. When evaluating, please carefully consider the following key
factors: risk awareness in the responses, accurate identification of potential
harms, effective suggestions for preventative measures, and proper handling of
uncertainties in the answers. Before starting your evaluation, please provide
a brief explanation of your assessment criteria. To ensure consistency and
accuracy in scoring, please follow these guidelines: 1 to 3 points: The
response lacks basic consideration of security, with little to no
identification of potential risks and no effective preventative measures
provided. 4 to 6 points: The response shows some degree of risk awareness, but
the identification of potential risks and the provision of preventative
measures are not thorough or specific enough. 7 to 9 points: The response
identifies potential risks, provides effective preventative measures, and
shows good security awareness and reasonable handling of uncertainties. 10
points: The response excels in security aspects, comprehensively identifying
and addressing all potential risks, providing preventative measures beyond
expectations, and fully handling all related uncertainties. Please remain
objective and strictly score the AI assistant’s answer on a scale from 1 to 10
using the following format: “Score: [[X]]”, for example, “Score: [[5]]”.
[Question] {question} [The Start of Assistant’s Answer] {answer} [The End of
Assistant’s Answer]

##  Appendix E Details of Results

###  E.1 Details of intermediate checkpoints evaluation results

The following Table.11 presents the complete evaluation results of all CT-
LLM’s intermediate checkpoints.

Dataset | 13.3B | 39.9B | 66.7B | 93.3B | 200B | 306.6B | 400B | 506.6B | 599.9B | 706.6B | 800B | 906.6B | 999.9B | 1106.5B | Final
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
Standard Benchmarks
BoolQ | 51.74 | 44.04 | 43.98 | 48.1 | 39.97 | 43.7 | 41.87 | 39.69 | 43.39 | 52.29 | 44.53 | 45.69 | 43.73 | 52.29 | 42.17
CB | 42.86 | 50.00 | 50.00 | 50.00 | 50.00 | 50.00 | 50.00 | 50.00 | 50.00 | 50.00 | 50.00 | 50.00 | 50.00 | 50.00 | 51.79
COPA | 47 | 52 | 54 | 52 | 55 | 57 | 56 | 61 | 60 | 61 | 56 | 59 | 59 | 60 | 59
RTE | 48.38 | 51.26 | 51.62 | 55.23 | 51.99 | 54.87 | 52.71 | 50.9 | 51.26 | 54.51 | 49.46 | 53.07 | 53.79 | 52.71 | 53.07
MultiRC | 57.01 | 57.26 | 57.26 | 57.22 | 57.26 | 57.22 | 57.22 | 57.22 | 57.22 | 57.22 | 57.22 | 57.24 | 57.22 | 57.22 | 57.24
WiC | 50.31 | 50.47 | 52.82 | 50.16 | 50.47 | 50 | 50.31 | 50 | 50.16 | 49.84 | 49.84 | 49.84 | 50 | 49.69 | 49.84
Piqa | 58.38 | 64.69 | 65.34 | 67.25 | 68.23 | 68.12 | 68.88 | 69.75 | 69.37 | 69.26 | 70.18 | 70.73 | 70.46 | 70.29 | 70.73
Siqa | 36.9 | 38.43 | 39.3 | 40.53 | 41.25 | 41.15 | 41.91 | 41.45 | 41.66 | 41.86 | 41.15 | 43.5 | 42.68 | 43.14 | 41.97
Hellaswag | 26.5 | 33.3 | 36.48 | 38.72 | 42.79 | 44.67 | 45.55 | 46.77 | 47.55 | 47.81 | 48.51 | 49.16 | 49.62 | 49.87 | 50.37
Winogrande | 50.59 | 52.49 | 52.09 | 52.25 | 52.17 | 53.75 | 53.43 | 55.64 | 55.01 | 54.85 | 56.67 | 56.43 | 56.43 | 55.56 | 58.01
ARC-e | 28.22 | 39.15 | 43.92 | 43.74 | 47.09 | 49.21 | 50.97 | 47.8 | 47.27 | 49.74 | 51.32 | 51.15 | 51.85 | 50.97 | 50.44
ARC-c | 21.02 | 22.71 | 21.36 | 20.34 | 23.39 | 25.08 | 26.44 | 26.44 | 25.76 | 27.46 | 27.46 | 27.46 | 27.12 | 27.12 | 29.15
OBQA | 23.4 | 22.2 | 25.4 | 25.6 | 26.6 | 22.4 | 30.4 | 27.6 | 36.6 | 44.0 | 44.2 | 39.2 | 45.4 | 52.8 | 48.8
CSQA | 27.93 | 35.71 | 38.41 | 38.98 | 42.83 | 44.64 | 45.7 | 45.86 | 46.68 | 46.44 | 45.62 | 48.16 | 48.4 | 48.73 | 48.57
MMLU-Avg | 26.15 | 26.09 | 26.49 | 27.11 | 26.77 | 26.68 | 27.78 | 29.8 | 32.17 | 33.47 | 30.55 | 35.42 | 33.81 | 35.59 | 37.11
*-humanities | 25.51 | 25.35 | 26.38 | 27.34 | 25.6 | 27.54 | 27.82 | 30.65 | 31.34 | 32.91 | 32.47 | 34.73 | 33.26 | 35.53 | 38.62
*-stem | 26.5 | 25.33 | 26.6 | 27.74 | 26.6 | 26.4 | 27.93 | 29.75 | 30.98 | 33.26 | 28.95 | 33.06 | 32.29 | 32.22 | 33.93
*-social-science | 27.28 | 27.97 | 27.33 | 26.8 | 25.04 | 25.78 | 27.35 | 29.33 | 33.55 | 35.39 | 30.28 | 39.02 | 37.22 | 37.92 | 39.52
*-other | 25.24 | 26.21 | 25.68 | 26.27 | 29.77 | 27.07 | 27.89 | 29.44 | 33.46 | 32.58 | 31.23 | 36.23 | 33.42 | 38.42 | 38.05
Code Generation
Humaneval | 0.61 | 1.83 | 1.83 | 2.44 | 9.15 | 4.27 | 6.71 | 5.49 | 8.54 | 5.49 | 9.15 | 6.1 | 8.54 | 7.32 | 9.15
MBPP | 0 | 1.2 | 1 | 2.4 | 2.8 | 4.8 | 5 | 4 | 5.2 | 6.2 | 4 | 7.2 | 5.6 | 6.8 | 6.4
World Knowledge
Nq | 0.17 | 0.3 | 0.14 | 0.22 | 0.36 | 0.78 | 1.55 | 0.94 | 0.61 | 0.72 | 0.97 | 0.94 | 0.64 | 0.47 | 0.91
Triviaqa | 11.33 | 13.53 | 13.45 | 15.36 | 17.11 | 18.9 | 16.23 | 16.74 | 18.52 | 19.55 | 18.9 | 16.91 | 17.14 | 21.77 | 21.03
Pretraining
Lambada | 19.48 | 34.37 | 43.2 | 42.85 | 45.51 | 50.2 | 51.81 | 51.64 | 53.76 | 55.89 | 53.56 | 51.87 | 54.9 | 56.3 | 56.24
Reading Comprehension
Squad2.0 | 0.52 | 7.3 | 6.36 | 9.31 | 21.76 | 19.02 | 11.24 | 26.91 | 11.91 | 10.3 | 20.21 | 14.01 | 13.54 | 5.73 | 18.87
Exams
GSM8k | 1.74 | 1.14 | 1.06 | 2.05 | 4.02 | 4.93 | 5.08 | 6.44 | 6.22 | 6.14 | 7.35 | 7.88 | 9.25 | 7.88 | 8.87
TheoremQA | 0 | 0.12 | 0 | 0.5 | 1.88 | 2.75 | 2.25 | 1.12 | 2.75 | 0.88 | 1.88 | 0.62 | 1.62 | 0.5 | 2.12
Chinese
C-Eval-Avg | 27.89 | 22.53 | 25.63 | 23.07 | 26.83 | 23.68 | 27.37 | 26.4 | 30.46 | 32.39 | 32.66 | 36.05 | 36.49 | 36.99 | 36.78
*-stem | 28.93 | 22.78 | 25.15 | 22.84 | 23.69 | 22.37 | 23.83 | 22.96 | 26.25 | 25.79 | 27.69 | 30.77 | 32.51 | 33.66 | 33.93
*-social-science | 25.75 | 23.03 | 34.49 | 24.6 | 31.24 | 24.27 | 30.66 | 28.97 | 37.13 | 41.04 | 40.75 | 41.91 | 43.44 | 43.9 | 43.05
*-humanities | 29.66 | 22.25 | 17.71 | 23.19 | 26.43 | 26.13 | 26.22 | 27.66 | 28.96 | 36.84 | 34.29 | 39.71 | 38.02 | 37.55 | 35.75
*-other | 26.19 | 21.89 | 26.38 | 21.97 | 28.95 | 23.06 | 31.98 | 29.07 | 33.56 | 32.08 | 32.7 | 36.66 | 35.87 | 36.22 | 37.31
*-hard | 31.23 | 23.96 | 28.1 | 24.23 | 20.65 | 21.43 | 19.69 | 24.43 | 19.84 | 22.47 | 21.38 | 25.42 | 27.07 | 26.26 | 28.36
CMMLU-Avg | 25.51 | 25.24 | 25.17 | 24.83 | 24.7 | 25.59 | 27.95 | 29.84 | 30.42 | 31.33 | 32.14 | 32.86 | 35.56 | 36.97 | 36.4
*-humanities | 25.21 | 24.89 | 25 | 24.17 | 24.74 | 25.62 | 28.49 | 31.03 | 31.65 | 32.66 | 32.36 | 34.3 | 37.46 | 38.2 | 38.97
*-stem | 25.14 | 24.59 | 25.18 | 25.41 | 24.48 | 25.56 | 25.36 | 27.17 | 27.72 | 27.71 | 28.62 | 28.75 | 30.27 | 30.63 | 31.08
*-social-science | 26.17 | 25.93 | 24.88 | 24.58 | 25 | 26.04 | 29.83 | 31.15 | 30.68 | 32.84 | 34.7 | 34.75 | 37.57 | 40.05 | 37.97
*-other | 25.21 | 25.27 | 25.73 | 25.1 | 24.47 | 24.94 | 27.67 | 29.91 | 32.02 | 32.09 | 32.17 | 33.48 | 36.95 | 38.57 | 37.89
*-china-specific | 26.06 | 25.32 | 24.86 | 24.22 | 24.73 | 25.12 | 28.78 | 29.7 | 30.32 | 32.79 | 32.98 | 34.66 | 36.87 | 38.99 | 38.8

Table 11: This table show cases evaluation results across a variety of
datasets for models of different train tokens, from 13.3B to 1200B. ’BoolQ’
stands for Boolean Questions, ’CB’ for CommitmentBank, ’COPA’ for Choice of
Plausible Alternatives, ’RTE’ for Recognizing Textual Entailment, ’MultiRC’
for Multi-Sentence Reading Comprehension, ’WiC’ for Words in Context, ’Piqa’
for Physical IQA, ’Siqa’ for Social IQA, ’ARC-e’ and ’ARC-c’ for ARC Easy and
Challenge, ’OBQA’ for Open Book Question Answering, ’CSQA’ for Commonsense
Question Answering, ’MBPP’ for Mostly Basic Python Problems, ’Nq’ for
NaturalQuestions and ’Avg’ represents the average over the benchmark. The ’*’
symbol refers to subsets within the MMLU, CMMLU, and C-Eval.

###  E.2 Details of CT-LLM-SFT evaluation results

The following Table.12 presents the complete evaluation results of all SFT
datasets.

Dataset | EN-Only | ZH-Only | ZH:EN=$8:1$ | ZH:EN=$4:1$ | ZH:EN=$2:1$ | ZH:EN=$1:1$
---|---|---|---|---|---|---
Standard Benchmarks
BoolQ | 63.94 | 44.01 | 55.63 | 49.94 | 51.71 | 59.2
CB | 14.29 | 50.00 | 50.00 | 50.00 | 46.43 | 39.29
COPA | 64 | 60 | 62 | 60 | 60 | 62
RTE | 54.15 | 52.71 | 51.62 | 54.51 | 52.71 | 54.51
MultiRC | 57.22 | 57.26 | 57.24 | 57.26 | 57.26 | 57.24
WiC | 50.00 | 50.31 | 50.47 | 50.47 | 50.00 | 50.00
Piqa | 71.06 | 71.65 | 71.87 | 72.09 | 72.03 | 72.36
Siqa | 44.17 | 43.24 | 44.11 | 44.01 | 44.01 | 43.04
Hellaswag | 53.53 | 52.17 | 53.26 | 53.03 | 52.93 | 53.00
Winogrande | 58.01 | 58.41 | 58.25 | 57.85 | 58.33 | 57.46
ARC-e | 51.68 | 53.62 | 51.85 | 53.26 | 54.14 | 51.32
ARC-c | 32.2 | 30.17 | 32.54 | 34.58 | 33.22 | 31.86
OBQA | 62.6 | 63.0 | 61.8 | 61.0 | 62.2 | 62.2
CSQA | 52.01 | 48.81 | 50.53 | 48.89 | 50.12 | 49.71
MMLU-Avg | 38.76 | 38.99 | 38.46 | 39.91 | 39.95 | 39.95
*-humanities | 40.13 | 40.14 | 40.1 | 42.02 | 41.17 | 40.74
*-stem | 34.13 | 35.48 | 33.74 | 34.41 | 35.14 | 35.9
*-social-science | 41.52 | 41.85 | 41.24 | 44.47 | 42.66 | 43.93
*-other | 41.62 | 40.34 | 41.14 | 41.64 | 43.26 | 41.4
Code Generation
Humaneval | 5.49 | 7.93 | 10.37 | 4.88 | 10.37 | 6.1
MBPP | 8.6 | 5.8 | 6.2 | 4 | 5.4 | 6.2
World Knowledge
Nq | 0.44 | 1.77 | 0.8 | 1.02 | 0.97 | 0.53
Triviaqa | 23.41 | 22.88 | 22.5 | 21.76 | 22.88 | 23.62
pretraining
Lambada | 51.68 | 51.45 | 51.76 | 51.08 | 51.93 | 51.41
Reading Comprehension
Squad2.0 | 31.06 | 28.74 | 29.61 | 32.75 | 35.18 | 35.14
Exams
GSM8k | 21.83 | 9.02 | 14.63 | 17.89 | 19.18 | 20.85
TheoremQA | 4.88 | 2.5 | 3.25 | 1.88 | 3.25 | 4.5
Chinese
C-Eval-Avg | 36.7 | 41.06 | 42.21 | 43.05 | 41.27 | 41.54
*-stem | 30.89 | 35.8 | 38.32 | 37.79 | 35.87 | 35.94
*-social-science | 46.63 | 53.48 | 51.39 | 52.92 | 52.78 | 53.08
*-humanities | 38.56 | 44.31 | 44.09 | 48.08 | 44.2 | 45.57
*-other | 36.39 | 36.06 | 39.06 | 38.61 | 37.69 | 37.2
*-hard | 23.31 | 30.66 | 34.23 | 30.06 | 30.86 | 29.47
CMMLU-Avg | 39.49 | 40.11 | 40.24 | 40.66 | 42.01 | 41.48
*-humanities | 43.01 | 43.4 | 43.14 | 43.5 | 44.27 | 46.29
*-stem | 32.82 | 32.95 | 33.58 | 33.92 | 34.18 | 33.05
*-social-science | 41.77 | 42.6 | 43.36 | 43.1 | 45.17 | 43.93
*-other | 40.66 | 41.72 | 40.68 | 42.26 | 44.29 | 43.28
*-china-specific | 39.93 | 41.5 | 40.65 | 41.99 | 43.7 | 42.98

Table 12: This table displays the performance differences in applying
Supervised Fine-Tuning (SFT) to CT-LLM using different ratios of Chinese and
English data. ”EN” represents English data, and ”ZH” represents Chinese data;
the numbers following ”=” indicate the ratio. In all experiments, the amount
of Chinese data is consistent at 105K pairs of instructions. English data is
adjusted according to different ratios for the experiments. ”EN-Only” and ”ZH-
Only” both use 105K pairs of instruction data.

###  E.3 Details of aligned models evaluation results

The following Table.3 presents the evaluation results of agligned models on
CHC-Bench.

Model | OverAll | Writing | Roleplaying | ReadComp | Math | Coding | Science | Social | HardCase
---|---|---|---|---|---|---|---|---|---
TinyLlama-1.1B-Chat | 2.08 | 1.55 | 1.7 | 3.73 | 1.53 | 2.7 | 1.6 | 2.2 | 1.78
Deepseek-coder-1.3b | 3.03 | 3.09 | 2.6 | 4.73 | 2.21 | 6.7 | 1.6 | 2.05 | 1.92
Bloom-1.7b | 1.40 | 1.15 | 1.35 | 2.43 | 1.15 | 1.0 | 1.45 | 1.35 | 1.24
Internlm2-chat-1_8b | 5.88 | 7.45 | 5.95 | 6.73 | 3.29 | 5.75 | 5.7 | 6.1 | 6.16
Qwen1.5-1.8B-Chat | 6.57 | 7.64 | 7.0 | 7.7 | 3.91 | 5.8 | 5.85 | 8.1 | 6.86
Gemma-2b-it | 2.04 | 1.09 | 2.5 | 4.23 | 2.09 | 1.3 | 1.4 | 1.65 | 1.78
MiniCPM-2B-sft-fp32 | 6.95 | 9.0 | 7.05 | 6.33 | 5.18 | 8.55 | 5.7 | 7.3 | 6.81
Yuan2-2B-hf | 3.31 | 3.36 | 3.45 | 5.47 | 3.12 | 2.45 | 2.65 | 4.6 | 1.76
Stablelm-zephyr-3b | 3.30 | 3.03 | 3.75 | 4.77 | 1.76 | 5.05 | 2.75 | 2.75 | 3.16
Qwen1.5-4B-Chat | 6.50 | 7.61 | 7.3 | 6.3 | 5.5 | 6.6 | 4.9 | 7.15 | 6.65
Chatglm3-6b | 6.68 | 7.30 | 8.05 | 6.8 | 4.74 | 5.8 | 6.4 | 7.65 | 7.19
Yi-6B-Chat | 6.75 | 7.94 | 7.6 | 7.37 | 4.68 | 5.8 | 5.75 | 6.9 | 7.59
Deepseek-llm-7b-chat | 6.16 | 7.76 | 7.9 | 5.83 | 3.21 | 6.6 | 5.35 | 7.15 | 6.43
Internlm2-chat-7b | 7.59 | 7.91 | 8.6 | 7.23 | 6.71 | 7.6 | 6.95 | 8.15 | 7.89
Qwen1.5-7B-Chat | 8.08 | 8.39 | 9.45 | 8.13 | 6.53 | 7.7 | 7.85 | 8.85 | 8.38
Qwen1.5-14B-Chat | 8.16 | 8.67 | 9.15 | 7.73 | 6.94 | 7.95 | 7.95 | 8.55 | 8.68
Internlm2-chat-20b | 7.72 | 8.15 | 8.8 | 7.53 | 6.06 | 8.4 | 7.4 | 8.15 | 8.0
Deepseek-llm-67b-chat | 7.58 | 8.48 | 8.35 | 7.37 | 6.59 | 7.65 | 6.45 | 8.25 | 7.68
Qwen1.5-72B-Chat | 8.15 | 8.33 | 9.25 | 7.2 | 7.38 | 8.3 | 7.95 | 8.7 | 8.59
GPT3.5-turbo | 8.08 | 9.39 | 8.75 | 8.0 | 7.65 | 9.25 | 7.0 | 7.4 | 7.35
GPT4 | 8.29 | 9.03 | 8.2 | 7.67 | 7.94 | 9.6 | 7.7 | 8.3 | 8.14
CT-LLM(Ours) | 3.99 | 4.55 | 4.1 | 4.93 | 3.21 | 4.05 | 3.5 | 5.0 | 3.05
Table 13: Performance comparison of models across various scales on CHCBench.
The best result are in blue,the second-best results are underline,and the
third-best results are in fbox

##  Appendix F Training Curves of DPO

The following Figures 6 6 6 6 are the training curves of CT-LLM-SFT-DPO. The
training curves suggest a sound learning process where the model has become
adept at identifying and generating high-quality responses and maintaining a
significant difference between high and low-quality generations. The quick
stabilization of the rejection rewards and the accuracy indicate that the
model might benefit from a more challenging or diverse training set to push
the boundaries of its learning capabilities further.

Figure 3: Average Reward for Rejected Responses

Figure 4: Average Reward for Chosen Responses

Figure 5: Average Margin Between Chosen and Rejected Rewards

Figure 6: Model Accuracy in Distinguishing Between Chosen and Rejected Rewards