# DCR-Consistency: Divide-Conquer-Reasoning for Consistency Evaluation and
Improvement of Large Language Models

Wendi Cui1, Jiaxin Zhang1,2, Zhuohang Li3, Damien Lopez1, Kamalika Das1,2,
Bradley Malin3,4, Sricharan Kumar1,2
1Intuit 2Intuit AI Research 3Vanderbilt University 4Vanderbilt University
Medical Center
{wendi_cui<EMAIL_ADDRESS><EMAIL_ADDRESS>Corresponding
Author. Our code is available at https://github.com/intuit-ai-research/DCR-
consistency.

###### Abstract

Evaluating the quality and variability of text generated by Large Language
Models (LLMs) poses a significant, yet unresolved research challenge.
Traditional evaluation methods, such as ROUGE and BERTScore, which measure
token similarity, often fail to capture the holistic semantic equivalence.
This results in a low correlation with human judgments and intuition, which is
especially problematic in high-stakes applications like healthcare and finance
where reliability, safety, and robust decision-making are highly critical.
This work proposes DCR, an automated framework for evaluating and improving
the consistency of LLM-generated texts using a divide-conquer-reasoning
approach. Unlike existing LLM-based evaluators that operate at the paragraph
level, our method employs a divide-and-conquer evaluator (DCE) that breaks
down the paragraph-to-paragraph comparison between two generated responses
into individual sentence-to-paragraph comparisons, each evaluated based on
predefined criteria. To facilitate this approach, we introduce an automatic
metric converter (AMC) that translates the output from DCE into an
interpretable numeric score. Beyond the consistency evaluation, we further
present a reason-assisted improver (RAI) that leverages the analytical reasons
with explanations identified by DCE to generate new responses aimed at
reducing these inconsistencies. Through comprehensive and systematic empirical
analysis, we show that our approach outperforms state-of-the-art methods by a
large margin (e.g., +19.3% and +24.3% on the SummEval dataset) in evaluating
the consistency of LLM generation across multiple benchmarks in semantic,
factual, and summarization consistency tasks. Our approach also substantially
reduces nearly 90% output inconsistencies, showing promise for effective
hallucination mitigation.

##  1 Introduction

Large language models (LLMs) such as GPT-4 and PaLM 2 (Yang et al., 2023;
Bubeck et al., 2023) have demonstrated impressive performance on a variety of
natural language generation (NLG) tasks, including summarization (Tam et al.,
2022), open-book question-answering (QA) (Kamalloo et al., 2023), and
retrieval-augmented generation (RAG) (Lewis et al., 2020; Liu et al., 2023a).
The evaluation of generated response quality often involves the assessment of
the semantic equivalence between two pieces of text, e.g., between the
generated response and the original text in summarization tasks or between two
candidate responses in open-book QA tasks. However, conventional evaluation
methods, such as BARTScore (Yuan et al., 2021) and BERTScore (Zhang et al.,
2020), which rely on token-level comparison, are inadequate for accurately and
reliably measuring the quality of generated content, particularly in complex
scenarios with long paragraphs (Liu et al., 2023b; Hanna & Bojar, 2021). To
address this issue, LLM-based evaluators such as G-Eval (Liu et al., 2023b)
and GPTScore (Jinlan et al., 2023) have proposed a new framework that
evaluates texts via paragraph-level comparison. While these evaluators show
promise for certain tasks, their scores often fail to achieve high concordance
with human judgments of semantic equivalence. Furthermore, as only numeric
scores are provided with no explanation, it can be challenging for humans to
trust or reason about these scores, particularly when using LLMs that are
known to hallucinate (Li et al., 2023; Ji et al., 2023; Rawte et al., 2023).

Assessing the consistency of LLMs is more broadly connected to AI safety and
has become a critical step in improving the reliability of these systems by
preventing the generation of misinformation and harmful content. Wang et al.
(2022) demonstrates that consistency checking can significantly enhance the
chain of thought reasoning in LLMs. Similarly, Kuhn et al. (2023) leverages
semantic consistency for uncertainty estimation in NLG. Recent studies employ
consistency checking to detect hallucinations based on pre-trained LLMs
(Manakul et al., 2023; Zhang et al., 2023a) and instruction-tuned LLMs
(Mündler et al., 2023). Although these methods exhibit promising results on
several specific tasks, including mathematical reasoning and factual
assessment, the potential failures (Chen et al., 2023) of self-consistency are
often overlooked. This is essentially due to a lack of a generic, automatic,
and reliable strategy that assesses the consistency of two responses, let
alone remediating such inconsistency after identifying them.

Figure 1: (a) Overview of the proposed DCR framework. The first two components
(DCE-AMC) aim at providing a better strategy for evaluating and quantifying
semantic consistency to best match human judgments. Building on this, a third
component RAI further utilizes analytical reasoning to iteratively improve the
consistency of LLM-generated content w.r.t. the reference by minimizing
hallucinations. (b) The combination of DCE and AMC (DCE-AMC-4) significantly
outperforms the baseline methods in terms of correlations with human ratings.
(c) RAI substantially reduces output inconsistencies by $\sim 90\%$ through a
single improvement iteration on SummEval and QAGS benchmarks.

In this paper, we introduce a novel framework, called Divide-Conquer-Reasoning
(abbreviated as DCR hereafter), for developing an automatic and reliable
consistency evaluation method. Our approach capitalizes on the intuition that
human evaluators typically assess consistency by comparing the semantic
meaning of the generated text to the reference text sentence by sentence and
then combining the analysis to make a holistic judgment of the complete
concept. Unlike existing metrics that rely on either token-level or paragraph-
level checks, our approach breaks down the paragraph-to-paragraph comparison
of the two pieces of text into a series of sentence-to-paragraph comparisons.
This approach avoids confusing LLM by either providing too much information at
once or zooming in too narrowly. Additionally, our approach does not rely on
LLMs to directly output verbal scores, which have been shown to be prone to
hallucination. We note that our AMC method is a reference-free method, which
does not rely on a golden reference written by the human expert. For example
in a summary task, our DCR method does not need a sample summary and can
compare directly between the target summary and the original paragraphs.

The DCR framework is composed of three components, each executed by an LLM
agent, as shown in Fig. 1. Given the reference and candidate, the Divide-
Conquer Evaluator (DCE) realizes the notion of divide-conquer to determine
whether the candidate is semantically equivalent to the reference at a
sentence level. DCE automatically partitions the candidate paragraph into
sentences (divide), evaluates each sentence against the reference paragraph
based on pre-defined semantic-level consistency criteria (conquer), and
generates a list of reasons that explain why each sentence is or is not
consistent with the reference paragraph. Next, the Auto-Metric Converter (AMC)
which builds upon DCE, converts the reasons (with explanations) into a numeric
score that is more aligned with human intuition and provides better
interpretation. The numeric score can be used to evaluate consistency in
various tasks, such as summarization, factual assessment, and hallucination
detection.

Our DCR framework not only evaluates consistency but also enhances it through
the Reason-Assisted Improver (RAI), a third LLM agent that utilizes the
outputs of DCE to generate new candidate sentences. By incorporating the
explanations provided by DCE with the original context, RAI produces sentences
that mitigate inconsistencies (hallucinations). This improvement process can
be iteratively applied by utilizing the re-evaluation produced by DCE to
ultimately achieve a candidate response that is fully aligned with the
reference text.

We conducted an evaluation of our approach on three different NLG tasks,
including semantic, summarization, and factual consistency evaluations. Our
results demonstrate that DCR significantly outperforms all existing baseline
methods as a consistency evaluator, with improvements of up to 19.3% and 24.3%
compared to G-Eval on the SummEval dataset. Additionally, our approach
achieved high correlations with human judgment on all three benchmarks.
Notably, we observed highly promising results in consistency improvement rate
(from 86.71% to 91.11%) at a substantially lower effort and cost due to its
multi-thread parallel implementation.

##  2 Preliminaries

#### Limitation of Existing Methods.

The conventional metrics, such as BERTscore and BARTscore, rely on a token-
level comparison using n-gram or contextual embedding to calculate cosine
similarity. However, this approach fails to capture the overall semantic
meaning as it directly aggregates token-level similarities. To address this
issue, leveraging the power of LLMs for self-evaluation has been proposed.
G-Eval (Liu et al., 2023b) and GPT-Eval (Jiang et al., 2023) evaluate
consistency at a paragraph level by prompting LLMs to compare two candidates
as a whole. However, these approaches have a major drawback as the generated
verbal scores by LLMs are prone to hallucinations, resulting in abnormally
higher ratings for LLM-generated content that diverge from human judgment (Liu
et al., 2023b). Such methods also generate no actionable insight to justify
the score or mitigate inconsistencies after identifying them.

#### Problem Formulation.

Given a user query $\mathcal{Q}$ and LLM model $\mathcal{M}$, let
$\mathcal{C}$ refer to the candidate response drawn from
$\mathcal{C}=\mathcal{M}(\mathcal{Q})$. LLM-generated responses are commonly
evaluated using some reference texts, denoted by $\mathcal{R}$, for instance,
human writing samples for generation tasks and original content for
summarization tasks. The objective of consistency evaluation is to build a
function $f$ that quantitatively measures the semantic equivalence
$\mathcal{S}$ between the generated candidates $\mathcal{C}$ and reference
$\mathcal{R}$ as
$\mathcal{S}=f(\mathcal{R},\mathcal{C}|\mathcal{Q},\mathcal{M})$ where
$\mathcal{S}$ could be binary decision, such as “Yes” or “No”, “Consistent” or
“Not Consistent”, or numeric score, e.g., [-1, +1]. However, it is worth
noting that our evaluation can be generally used to check consistency between
two candidates where both are generated by LLMs. In that scenario, we only
need to assume one candidate as the reference for self-check consistency.
Note that our proposed method employs the same setting as G-Eval (Liu et al.,
2023b), which does not rely on a golden reference written by the human expert
or ground truth labels. Our objective is to offer a generic way to assess the
consistency between two text sequences. For instance, in the summarization
task, one text sequence may be the original document, while the other might be
the generated text summarization by LLMs. To distinguish between the two text
sequences, we referred to one of the text sequences as “reference” and the
other as “candidate”.

Figure 2: An example of evaluating and improving the consistency of generated
text via DCR.

##  3 Divide-Conquer-Reasoning

To overcome the aforementioned limitations, we propose to evaluate and improve
the consistency of LLM output via a Divide-Conquer-Reasoning approach,
referred to as DCR. The approach comprises three key components, as
illustrated in Fig. 1: (1) DCE, which disassembles the candidate paragraph and
scrutinizes semantic inconsistencies sentence-by-sentence, (2) AMC, which
converts sentence-level inconsistency/consistency reasons into numeric scores
for quantitative interpretation, and (3) RAI, which conducts analytical
reasoning to improve consistency through candidate regeneration. Essentially,
our approach involves a combination of sentence-level analysis, semantic
consistency checking, and causal analysis, making it an ideal evaluation
metric for a diverse range of NLG tasks that require comparison to reference
texts, such as summarization, open-book question-answering (QA), and
retrieval-augmented generation.

Moreover, DCR not only evaluates but also improves the consistency of
generated text through analysis and reasoning, which aligns with human
intuition. Fig.2 provides an example of how DCR can evaluate and enhance the
consistency of candidate text. In the following sections, we will discuss each
component in detail.

###  3.1 Divide-Conquer Evaluator (DCE)

The Divide-Conquer Evaluator (DCE) is an LLM Agent designed to perform
semantic consistency checks between the reference and the candidate using a
sentence-by-sentence strategy. This agent accepts a reference paragraph and a
candidate paragraph as inputs, and employs a divide-conquer strategy to break
down the entire paragraph into multiple individual sentences (divide) and then
assess each sentence against the reference (conquer). More specifically, given
the input reference $\mathcal{R}=\langle s_{1}^{r},...,s_{l}^{r}\rangle$ and
candidate $\mathcal{C}=\langle s_{1}^{c},...,s_{k}^{c}\rangle$, we build a DCE
agent $\mathcal{L}_{\textup{{DCE}}}$ using the LLM model $\mathcal{M}$ (e.g.,
GPT-3.5/4) with an instructed prompt $\mathcal{P}_{\textup{{DCE}}}$ as:

$\\{\gamma_{1},\gamma_{2},...,\gamma_{k}\\}=\mathcal{L}_{\textup{{DCE}}}(\langle
s_{1}^{c},s_{2}^{c},...,s_{k}^{c}\rangle,\mathcal{R}~{}|~{}\mathcal{M},\mathcal{P}_{\textup{{DCE}}}).$
(1)

Eq.1 generates reasons, denoted as
$\Gamma=\\{\gamma_{1},\gamma_{2},...,\gamma_{k}\\}$, which is a list of
reasons explaining why each sentence $s_{i}^{c}(i=1,2,...,k)$ is or is not
consistent against the entire reference paragraph $\mathcal{R}$. It’s
important to note that the reasons $\gamma_{i}$ might comprise a short
paragraph containing multiple explanation sentences. We can tailor instruction
prompts by defining task-specific criteria to accommodate different tasks.
Table 1 provides an example of a prompt example with pre-defined criteria for
the summarization consistency task.

Table 1: Summarization Consistency Divide-Conquer Evaluator Prompt Your task
is to evaluate whether the summary is consistent with the article. You will
evaluate it by going through each sentence of the summary and check against
the following procedures:
---
• Understands all the aspects of the sentence, and compare if each aspect
exists in the article
• If it does, compare if the information in this sentence is consistent with
what is in the article
• Compare if all the information in this sentence can be directly inferred or
entailed from what is in the article. It is OK that not all information from
the article exists in this summary

We note that the sentence-level comparison used in DCE is not to compare each
sentence in the candidate text sequence to each sentence from the reference
text sequence but to compare each sentence in the candidate text sequence to
the entire reference text sequence. This design reduces the number of
comparison operations and ensures semantic consistency. For instance, in the
summarization task, DCE is to check if the candidate summaries are consistent
with the source article. This is achieved by checking if each sentence in the
candidate summaries is consistent with the entire original articles, rather
than a sentence-by-sentence matching. Thus, our method does not rely on any
sentence-matching techniques, such that there is no issue with the varying
number of sentences (Amplayo et al., 2022).

###  3.2 Auto-Metric Converter (AMC)

The Auto-Metric Converter (AMC) is an LLM Agent that aims to quantitatively
measure the consistency evaluation derived from the Divide-Conquer Evaluator
(DCE) by converting the reasons from DCE into a numeric score system. This is
accomplished by introducing an LLM agent, denoted as
$\mathcal{L}_{\textup{{AMC}}}$, which takes reasons
$\langle\gamma_{1},\gamma_{2},...,\gamma_{k}\rangle$ with an instructed prompt
$\mathcal{P}_{\textup{{AMC}}}$ as inputs:

$\\{z_{1},z_{2},...,z_{k}\\}=\mathcal{L}_{\textup{{AMC}}}(\\{\gamma_{1},\gamma_{2},...,\gamma_{k}\\}~{}|~{}\mathcal{M},\mathcal{P}_{\textup{{AMC}}}).$
(2)

The LLM Agent $\mathcal{L}_{\textup{{AMC}}}$ functions as a binary sentiment
classifier that classifies the reasons
$\langle\gamma_{1},\gamma_{2},...,\gamma_{k}\rangle$ to be either positive
(marked by “+1" if the sentence is consistent), or negative (marked by “-1”
otherwise). As a result, AMC outputs an array of scores
$\\{z_{1},z_{2},...,z_{k}\\},z_{i}\in\\{-1,+1\\}$ for each sentence $\langle
s_{1}^{c},s_{2}^{c},...,s_{k}^{c}\rangle$ in the candidate $\mathcal{C}$. We
then utilize this score array to calculate a comprehensive score $\mathcal{Z}$
to evaluate how consistent the candidate (paragraph) is against the reference
(paragraph):

$\mathcal{Z}=\left(\sum_{i=1}^{k}z_{i}+\alpha\right)/(k+\beta),\quad\hat{\mathcal{Z}}=(\mathcal{Z}+1)/2,\quad\hat{\mathcal{Z}}\in[0,1]$
(3)

where $k$ is the length of the score array, i.e., the number of sentences in
the candidate paragraph. Depending on the prompt, the reasons output by DCE
may not all be on the sentence level. To ensure that the score calculated is
solely generated by sentence-level reasons, we introduce $\alpha$ and $\beta$
in Eq. 3, as explained in detail in Appendix A.4. Finally, we rescale
$\mathcal{Z}$ to obtain the final score $\hat{\mathcal{Z}}$ that is typically
between 0 (completely inconsistent) and 1 (completely consistent). The closer
this score $\hat{\mathcal{Z}}$ is to 0, the more inconsistent the candidate
$\mathcal{C}$ is against the reference $\mathcal{R}$.

The AMC component serves as a binary sentiment classifier that classifies the
reasons to be either positive (marked by “+1" if the sentence is consistent),
or negative (marked by “-1” otherwise) for each sentence, then utilizes this
score array to calculate a comprehensive score to evaluate consistency in a
regression manner. Such a numerical score calculated by AMC is more stable
than the verbal score directly output by LLMs, as is verified by our
experiments. This design deliberately excludes the use of LLM in crucial steps
associated with generating numerical evaluation scores. Instead, it harnesses
the capabilities of LLMs for tasks in which they have demonstrated excellence,
specifically in reasoning.

###  3.3 Reason-Assisted Improver (RAI)

The Reason-Assisted Improver (RAI) is an LLM Agent that focuses on improving
the consistency of candidate sentences by reasoning through the inconsistent
explanations generated by the Divide-Conquer Evaluator (DCE). To achieve this
goal, we propose an LLM agent $\mathcal{L}_{\textup{{RAI}}}$ to generate new
candidate sentences
$\langle\hat{s}_{1}^{c},\hat{s}_{2}^{c},...,\hat{s}_{k}^{c}\rangle$ based on
the collected reasons $\\{\gamma_{1},\gamma_{2},...,\gamma_{k}\\}$ and
original sentences $\langle s_{1}^{c},s_{2}^{c},...,s_{k}^{c}\rangle$:

$\langle\hat{s}_{1}^{c},\hat{s}_{2}^{c},...,\hat{s}_{k}^{c}\rangle=\mathcal{L}_{\textup{{RAI}}}(\\{\gamma_{1},\gamma_{2},...,\gamma_{k}\\},~{}\langle
s_{1}^{c},s_{2}^{c},...,s_{k}^{c}\rangle,\mathcal{R}~{}|~{}\mathcal{M},\mathcal{P}_{\textup{{RAI}}}).$
(4)

The core task of $\mathcal{L}_{\textup{{RAI}}}$ is to rewrite the original
sentence $s_{i}^{c}$ if $s_{i}^{c}$ is inconsistent with the reference
$\mathcal{R}$ and return a new generated $\hat{s}_{i}^{c}$
($\hat{s}_{i}^{c}\neq s_{i}^{c}$), otherwise retain $s_{i}^{c}$. The newly
generated responses
$\hat{\mathcal{C}}=\langle\hat{s}_{1}^{c},\hat{s}_{2}^{c},...,\hat{s}_{k}^{c}\rangle$
can be considered as the consistency-improved candidate, which can be re-
evaluated by DCE to check if $\hat{\mathcal{C}}$ mitigates inconsistencies
against the reference $\mathcal{R}$.

The improved candidate $\hat{\mathcal{C}}$ in Eq.4 can be directly fed to the
DCE agent in Eq.1 after the first-round DCR , i.e., DCE$\rightarrow$
AMC$\rightarrow$ RAI. A straightforward extension is multi-round consistency
improvement, where the consistency is iteratively improved until reaching the
maximum number of rounds $m$. Algorithm 1 illustrates the workflow of the DCR
framework, which consists of three core components: DCE, AMC, and RAI.

Algorithm 1 Proposed Divide-Conquer-Reasoning (DCR) framework

Requirements: Candidate $\mathcal{C}$, Reference $\mathcal{R}$, LLM model
$\mathcal{M}$, LLM agents $\mathcal{L}_{\textup{{DCE}}}$,
$\mathcal{L}_{\textup{{AMC}}}$, $\mathcal{L}_{\textup{{RAI}}}$ with instructed
prompts $\mathcal{P}_{\textup{{DCE}}}$, $\mathcal{P}_{\textup{{AMC}}}$ and
$\mathcal{P}_{\textup{{RAI}}}$, and the maximum number of rounds $m$

for rounds $r=1,...,m$ do

Disassemble candidate $\mathcal{C}$ into sentences $\langle
s_{1}^{c},s_{2}^{c},...,s_{k}^{c}\rangle$, evaluate sentence-level consistency
against reference $\mathcal{R}$, and return the reasons
$\\{\gamma_{1},\gamma_{2},...,\gamma_{k}\\}\leftarrow\mathcal{L}_{\textup{{DCE}}}(\langle
s_{1}^{c},s_{2}^{c},...,s_{k}^{c}\rangle,\mathcal{R}~{}|~{}\mathcal{M},\mathcal{P}_{\textup{{DCE}}})$
in Eq. 1

Transform reasons into numeric scores
$\\{z_{1},z_{2},...,z_{k}\\}\leftarrow\mathcal{L}_{\textup{{AMC}}}(\\{\gamma_{1},\gamma_{2},...,\gamma_{k}\\}~{}|~{}\mathcal{M},\mathcal{P}_{\textup{{AMC}}})$
in Eq. 2

Calculate the final consistency evaluation score $\hat{\mathcal{Z}}$ based on
$\\{z_{1},z_{2},...,z_{k}\\}$ using Eq. 3

Generate improved candidate
$\langle\hat{s}_{1}^{c},\hat{s}_{2}^{c},...,\hat{s}_{k}^{c}\rangle\leftarrow\mathcal{L}_{\textup{{RAI}}}(\\{\gamma_{1},\gamma_{2},...,\gamma_{k}\\},~{}\langle
s_{1}^{c},s_{2}^{c},...,s_{k}^{c}\rangle,\mathcal{R}~{}|~{}\mathcal{M},\mathcal{P}_{\textup{{RAI}}})$

Update the candidate $\langle
s_{1}^{c},s_{2}^{c},...,s_{k}^{c}\rangle\leftarrow\langle\hat{s}_{1}^{c},\hat{s}_{2}^{c},...,\hat{s}_{k}^{c}\rangle$
and return Step 2

return $\hat{\mathcal{Z}}$,
$\langle\hat{s}_{1}^{c},\hat{s}_{2}^{c},...,\hat{s}_{k}^{c}\rangle$

##  4 Experiments

###  4.1 Benchmarks and Implementation Details

We utilize GPT-3.5 (gpt-3.5-turbo) and GPT-4 (gpt-4) as our LLM agents, and
the evaluations are carried out using the Azure OpenAI API. We set the
temperature to 0.0 to generate responses via the greedy algorithm. The
specific prompts used for each LLM agent are detailed in the Appendix (from
Table 7 to Table 12). All experiments are conducted on our local machine
(Macbook-Pro with M1 chip) without the need for GPU resources. In our
experimental setup, we set both $\alpha$ and $\beta$ in Eq. 3 to 0. We employ
four datasets to evaluate DCR where QQP and PAWS are binary datasets, as well
as SummEval and QAGS have numeric scores representing human judgments.

  * •

QQP and PAWS: Quora Question Pair corpus (Iyer et al., 2017) and the
Paraphrase Adversaries from Word Scrambling dataset (amd Jason Baldridge & He,
2019) contain pairs of sentences labeled to indicate whether they are
paraphrases or not, while PAWS specifically focuses on the adversarial
paraphrases. Following the guidance of BERTScore (Zhang et al., 2020), we are
using the PAWS development set and the first 5000 from the training set of
QQP.

  * •

SummEval (Fabbri et al., 2021) is a standard dataset that assesses various
summarization evaluation techniques. It gathers human ratings in various
aspects and is built on the CNN/DailyMail dataset (Hermann et al., 2015). In
this study, we mainly focus on the consistency evaluation.

  * •

QAGS (Wang et al., 2020) serves as a benchmark for assessing hallucinations in
summarization tasks. Its objective is to evaluate the consistency aspect of
summaries across two distinct summarization datasets: QGS-CNN and QAGA-XSUM.

Here we provide a detailed explanation of the “reference” used in our
experiments. For Paraphrase detection tasks, such as the QQP dataset, each
question pair is annotated with a binary value indicating whether the two
questions are paraphrases of each other. We consider “question1” as the
“reference” and “question2” as the “candidate”, and our task is to evaluate if
the candidate is consistent with the reference in semantic meaning. For
Summarization tasks, SummEval datasets include original source articles,
machine summaries, and human summaries. Our “reference” in this task is the
original source article, and our “candidate” is the machine summaries. Our
task is to check the factual consistency between them without relying on any
additional golden reference or ground truth.

###  4.2 Baseline Methods

We evaluate DCR against a variety of evaluation metrics and LLM-based
evaluators that have achieved state-of-the-art performance.

  * •

ROUGE (Lin, 2004) is widely used evaluation metric with three different
variants ROUGE-1, ROUGE-2, and ROUGE-L. We are using ROUGE-2 and ROUGE-L as
comparisons in our study.

  * •

BERTScore (Zhang et al., 2020) calculates the similarities between two pieces
of text using the contextualized embedding derived from the BERT model(Devlin
et al., 2019). It operates as a similarity-based assessment tool, which has
been widely used for various applications.

  * •

MoverScore (Zhao et al., 2019) enhances BERTScore by incorporating soft
alignments and introducing new aggregation techniques to provide a more robust
similarity assessment.

  * •

BARTScore (Yuan et al., 2021) is a comprehensive evaluator that uses the
average likelihood of the model’s output as its measurement criteria.

  * •

UniEval (Zhong et al., 2022) is a consolidated evaluator capable of assessing
various elements of text generation as QA tasks. It manages diverse evaluation
tasks by modifying the question format.

  * •

GPTScore (Jinlan et al., 2023) is an LLM-based evaluator that assesses texts
using pre-training models, e.g., GPT-3, and is designed to provide a higher
likelihood to high-quality generated text.

  * •

G-Eval (Liu et al., 2023b) is another LLM evaluator that utilizes LLMs with a
chain-of-thoughts (CoT) approach with a form-filling paradigm to evaluate the
quality of NLG outputs.

###  4.3 Main results on Consistency Evaluation (DCE-AMC)

#### Semantic Consistency Evaluation.

Table 2 shows the Area Under the ROC curve (AUROC) for automatic baseline
metrics and our method, following the practice of BERTScore (Zhang et al.,
2020). We note that while most metrics from BERTScore perform acceptably on
QQP, they exhibit a significant performance drop on PAWS. This suggests that
these baseline metrics struggle to detect the challenging adversarial examples
from a semantic consistency perspective. In contrast, our method, whether
implemented with GPT-3.5 or GPT-4, outperforms all the baseline metrics on
both QQP and PAWS, without a significant drop. Notably, DCE-AMC-4 demonstrates
superior robustness in adversarial paraphrase classification (semantic
consistency) achieving a relatively large improvement (+4.6% in QQP and +9.9%
in PAWS) compared to BERTScore.

Table 2: AUROC results on QQP and PAWS Metrics | QQP | PAWS
---|---|---
BLEU (Papineni et al., 2002a) | 0.707 | 0.527
METEOR (Banerjee & Lavie, 2005) | 0.755 | 0.532
ROUGE-L (Lin, 2004) | 0.740 | 0.536
CHRF++ (Popović, 2015) | 0.577 | 0.608
BEER (Stanojević & Sima’an, 2014) | 0.741 | 0.564
EED (Stanchev et al., 2019) | 0.743 | 0.611
CharacTER (Wang et al., 2016) | 0.698 | 0.650
BERTScore (Zhang et al., 2020) | 0.777 | 0.693
DCE-AMC-3.5 (our method) | 0.788 | 0.770
DCE-AMC-4 (our method) | 0.823 | 0.792

#### Factual Consistency Evaluation.

While advanced NLG models are capable of generating high-quality responses,
LLMs are known to occasionally produce non-factual statements or hallucinate
facts, which can undermine trust in their output. Recent work (Manakul et al.,
2023) has been conducted to identify such inconsistencies in terms of
factuality. To verify the effectiveness of our method in evaluating
hallucination, we test it on the QAGS benchmark, which includes two
summarization datasets: QAGS-CNN and QAGS-XSUM. Table 4 provides a
comprehensive comparison of various metrics based on Pearson, Spearman, and
Kendall-Tau correlations. We observe that BARTScore performs competitively on
the extractive subset (QAGS-CNN) but fails to demonstrate a high correlation
on the abstractive subset (QAGS-XSUM). UniEval exhibits a better correlation
than G-Eval-3.5 but is comparable to G-Eval-4. Our proposed DCE-AMC-4
outperforms all the baseline methods on both subsets, particularly by a
significant margin on QAGS-XSUM. Unlike the G-Eval method, which shows a
larger gap between GPT-3.5 and GPT-4, our DCE-AMC method remains relatively
stable when switching between LLMs.

It’s crucial to note that QAGS-XSUM is an abstractive dataset, and its
summaries are typically one sentence long. This contrasts with the extractive
database of QAGS-CNN, where summaries are composed of multiple sentences.
Consequently, our method operates at a sentence level for QAGS-XSUM, and our
final score is always either 0 or 1. Furthermore, the binary label in QAGS-
XSUM implies that we achieve the same correlation score using different
correlation methods.

Table 3: Correlation ($\rho$ and $\tau$) results of different metrics on SummEval benchmark. Metrics | SummEval-Consistency
---|---
Spearman ($\rho$) | Kendall-Tau ($\tau$)
ROUGE-2 | 0.187 | 0.155
ROUGE-L | 0.115 | 0.092
BARTScore | 0.382 | 0.315
BERTScore | 0.110 | 0.090
MoverScore | 0.152 | 0.127
UniEval | 0.446 | 0.371
GPT-Score | 0.449 | -
G-Eval-3.5 | 0.386 | 0.318
G-Eval-4 | 0.507 | 0.425
DCE-AMC-3.5 | 0.592 | 0.563
DCE-AMC-4 |  0.700 (+19.3%) |  0.668 (+24.3%)

#### Summarization Consistency Evaluation.

We follow the setting of previous work (Zhong et al., 2022) to evaluate
different summarization consistency using summary-level Spearman ($\rho$) and
Kendall-Tau ($\tau$) correlation. As shown in Table 3, baseline metrics using
semantic similarity, such as ROUGE and BERTScore, perform poorly on
consistency evaluations. While LLM-based evaluators like GPT-Score and G-Eval
exhibit higher correlations, they still underperformed compared to our
proposed method. DCE-AMC-4 achieves much higher human correspondence compared
to DCE-AMC-3.5 on both Spearman and Kendall-Tau correlation, which indicates
that the larger size of GPT-4 model is beneficial for sentence-level
consistency checking in summarization tasks. DCE-AMC-4 with stronger
correlations of $\rho=0.7$ and $\tau=0.668$, substantially improves upon the
G-Eval-4 baseline by a large margin (+19.3% and +24.3% respectively).

Table 4: Pearson ($r$), Spearman ($\rho$), and Kendall-Tau ($\tau$)
correlations of different baseline metrics on QAGS-CNN and QAGS-XSUM
benchmark.

Metrics | QAGS-CNN | QAGS-XSUM
---|---|---
Pearson ($r$) | Spearman ($\rho$) | Kendall-Tau ($\tau$) | Pearson ($r$) | Spearman ($\rho$) | Kendall-Tau ($\tau$)
ROUGE-2 | 0.459 | 0.418 | 0.333 | 0.097 | 0.083 | 0.068
ROUGE-L | 0.357 | 0.324 | 0.254 | 0.024 | -0.011 | -0.009
BARTScore | 0.735 | 0.680 | 0.557 | 0.184 | 0.159 | 0.130
BERTScore | 0.576 | 0.505 | 0.399 | 0.024 | 0.008 | 0.006
MoverScore | 0.414 | 0.347 | 0.271 | 0.054 | 0.044 | 0.036
UniEval | 0.682 | 0.662 | 0.532 | 0.461 | 0.488 | 0.399
G-Eval-3.5 | 0.477 | 0.516 | 0.410 | 0.211 | 0.406 | 0.343
G-Eval-4 | 0.631 | 0.685 | 0.591 | 0.558 | 0.537 | 0.472
DCE-AMC-3.5 | 0.699 | 0.648 | 0.596 | 0.573 | 0.573 | 0.573
DCE-AMC-4 | 0.782 | 0.760 | 0.706 | 0.602 | 0.602 | 0.602

###  4.4 Results for Consistency Improvement (RAI)

After implementing DCE and AMC, we can quantitatively determine whether each
candidate is consistent (score = 1) to the reference or not (score <1). Table
5 offers a statistical analysis of the number of inconsistent data after
evaluations (DCE-AMC), revealing 286, 111, and 86 inconsistent candidates for
the SummEval, QAGS-CNN, and QAGS-XSUM benchmarks respectively. Identifying
these inconsistent candidates is valuable but the more critical objective is
how to improve these responses to align with the references. To achieve this
goal, we re-generate a new response by implementing RAI based on the reasons
provided by DCE, and then use DCE to re-evaluate these improved responses. We
observe a significant improvement, with most inconsistencies corrected,
specifically 84 out of 86 examples on the QAGS-XSUM benchmark. The rate of
consistency improvement is $86.71\%$, $88.29\%$, and $97.67\%$ on SummEval,
QAGS-CNN, and QAGS-XSUM respectively. These impressive results demonstrate
that our reasoning approach RAI not only provides better consistency
evaluation metrics that align more closely with human judgments, but also
sheds light on improving consistency beyond evaluation. This finding is
particularly crucial for mitigating hallucination once we detect non-factual
statements via consistency checks. It’s worth noting that our reasoning method
RAI is a generic component that can also be applied directly at the paragraph
level, and the improvement in this context is significant as well, as
illustrated in Table 5.

Table 5: Performance of consistency improvement with RAI on three benchmark datasets.  Dataset (size) | SummEval (1600) | QAGS-CNN (236) | QAGS-XSUM (239)
---|---|---|---
| Sentence | Paragraph | Sentence | Paragraph | Sentence | Paragraph
Inconsistent data | 286 | 209 | 111 | 68 | 86 | 90
Corrected data with RAI | 248 | 198 | 89 | 64 | 84 | 82
Consistency improvement rate | 86.71% | 94.73% | 88.29% | 94.11% | 97.67% | 91.11%

###  4.5 Analysis

#### Why DCR Prefers Sentence-level Evaluation?

To further assess the potential advantage of the sentence-level approach in
consistency checking, we employed the same logic of outputting decisions and
reasons as used in DCE and developed an evaluator at the paragraph level, with
prompts provided in Appendix (Table 11). The comparative results between
paragraph level and sentence level can be viewed in Fig. 3. While the recall
of paragraph-level evaluation is higher on SummEval and QAGS-CNN benchmarks,
its overall performance in terms of the F1 score and precision is lower than
that of sentence-level evaluations, particularly on the QAGS benchmark. This
combination of higher recall and lower precision implies that more candidates
are incorrectly marked as consistent. In the task of consistency checking, a
metric with low recall and high precision (sentence level) is preferable as it
contributes to higher safety compared to a metric with high recall and low
precision (paragraph level), erring on the side of caution.

Figure 3: F1 score, precision, and recall performance of our method on
sentence-level and paragraph-level evaluations. We also verify the
effectiveness of the auto-metric converter.

In addition to superior accuracy, sentence-level evaluations can facilitate
more thorough inconsistency remediation when integrating with RAI. We compared
the performance improvement between our sentence level DCE and paragraph
level, as indicated in Table 5. Despite the higher recall of the paragraph-
level approach, fewer items are flagged as inconsistent, resulting in fewer
candidates being corrected, even though the improvement rate is higher. In
fact sentence level DCE leads to 25.25% and 39.05% more corrections compared
to the paragraph-level approach in SummEval and QAGS-CNN respectively.
Therefore, our sentence-level approach not only outperforms in terms of F1
score and precision during consistency checks, but also facilitates
comprehensive improvements through RAI.

#### Is Auto-metric Converter Necessary?

We present a comparison of our method, both with and without AMC, as shown in
Fig. 3. We observe that our method with only the DCE (red bar) performs
marginally better on the SummEval dataset but underperforms DCE-AMC (orange
bar) on all other benchmarks. Although DCE plays a key role in our method, the
AMC component is still desirable and highly necessary not only because it
shows better performance, but also because it facilitates the conversion of
reasons outputted by DCE to a numeric system. This conversion is both user-
friendly and practical, making it easy for humans to understand and apply.
Furthermore, it provides a straightforward means of evaluating the
effectiveness of the DCE component.

Figure 4: Multi-round consistency improvement

Figure 5: Computational cost.

#### Multi-round Consistency Improvement.

Table 5 showcases encouraging results on consistency improvement via RAI. This
naturally leads to the question: can we further enhance the consistency
through multiple rounds of RAI? Fig. 5 shows our investigation on multi-round
consistency improvement by iteratively applying RAI. It is noteworthy that the
convergence of consistency improvement is remarkably swift, achieving nearly
100% in just two rounds. The convergence rate on the QAGS datasets is highly
consistent across both subsets, slightly surpassing SummEval due to its high
initial rate after the first round of RAI. This is also corroborated by the
frequency distribution of the consistency score (Fig. 5 (right)). As the
number of rounds increases, the lower consistency scores (<1) gradually
decrease, and more inconsistent candidates tend to be consistent, where the
score is 1.

#### The Effect of LLM models.

We evaluated the performance of our method using different LLMs across all
three benchmarks. It is noteworthy that DCE-AMC-4 generally outperforms DCE-
AMC-3.5 across most datasets. The performance gap between the two LLM models
is relatively minor in terms of semantic consistency (QQP and PAWS in Table
2), and the abstractive subset (QAGS-XSUM in Table 4) in factual consistency,
but a significant difference is observed in summarization consistency in Table
3. This suggests that GPT-4 can further enhance performance, especially for
more complex evaluation tasks. As such, we applied RAI with GPT-4 directly to
verify its superior capability in consistency improvement. Nonetheless, the
benefits of GPT-3.5, such as higher computational efficiency and lower API
costs, should not be overlooked.

#### Computational Cost.

We assessed the computational cost of our method based on wall-clock time,
which is primarily consumed by LLMs inference. However, the divide-conquer
strategy we employed is scalable and easily implemented in parallel. Fig. 5
illustrates the computational cost of GPT-3.5 and GPT-4 with varying numbers
of threads on the QAGS-CNN benchmark. A clear reduction in computational cost
is observed as the number of threads increases. It’s important to note that
the decrease in time is more significant when transitioning from a single
thread to four threads, but tends to plateau as more threads are utilized.
While GPT-3.5, being the smaller LLM, is a more efficient option, GPT-4 often
delivers better performance.

##  5 Related Work

#### LLM-based Evaluations.

Unlike conventional evaluating metrics leveraging token-level or similarity
embeddings, such as BLEU (Papineni et al., 2002b), ROUGE (Lin, 2004), or
BERTScore (Zhang et al., 2020), recent proposed LLM-based evaluators (Wang et
al., 2023), such as GPTScore (Jinlan et al., 2023) and G-Eval (Liu et al.,
2023b), have demonstrated competitive performance on multiple NLG tasks. Their
idea is to utilize the LLMs to score the candidate output under the assumption
that the LLMs have learned to assign higher probabilities to fluent and high-
quality contexts. However, these LLM evaluators often exhibit lower
correlations with human judgments, and their reliability, robustness, and
validity remain under-explored (Liu et al., 2023b). Specifically, LLM
evaluators may pose potential risks of producing hallucinated or
overconfidence scores if the LLM model is not well calibrated for complex
tasks (Kadavath et al., 2022; Zhou et al., 2023). This results in limited
confidence in using LLM evaluators to directly evaluate paragraph-level
responses. Our proposed DCR framework addresses these challenges through a
divide-conquer strategy (DCE) coupled with a numeric score system (AMC). Our
method quantitatively evaluates paragraphs sentence-by-sentence and does not
rely on LLMs to directly output numeric scores, thus providing a more accurate
and comprehensive score that better aligns with human feedback.

#### Consistency Evaluations.

Consistency checking plays an essential role in a wide range of NLG tasks,
including question-answering (Durmus et al., 2020; Wang et al., 2020), factual
knowledge extraction (Elazar et al., 2021), summarization (Durmus et al.,
2020) and hallucination detection (Manakul et al., 2023). However, due to
various limitations of existing methods, such as reliance on additional pre-
trained models or question sets (Durmus et al., 2020), it is highly desirable
to develop a unified and automatic consistency metric (Wang et al., 2022). Our
proposed framework successfully fills this gap and demonstrates superior
performance compared to state-of-the-art baselines (Jinlan et al., 2023; Liu
et al., 2023b; Wang et al., 2023). More importantly, our proposed RAI enables
consistency improvement where the re-generated candidate response
significantly helps mitigate LLM hallucinations (Dhuliawala et al., 2023;
Mündler et al., 2023; Zhang et al., 2023b) in summarization, and open-book QA
tasks (Li et al., 2023).

##  6 Conclusion and Discussion

We proposed a general evaluation framework based on a divide-and-conquer
strategy for assessing the consistency between the LLM-generated output and
the reference texts across various NLG tasks. Moreover, the proposed method
can leverage analytical reasoning to generate revised text with improved
consistency. Through comprehensive and systematic empirical study across
multiple benchmarks in semantic, factual, and summarization consistency tasks,
we demonstrated that our approach significantly outperforms existing methods
in evaluating and enhancing the consistency of LLM-generated content. Despite
these advancements, we acknowledge several potential limitations of our
proposed method:

Not a Silver Bullet. While our sentence-level approach (DCE-AMC) excels in
evaluating consistency and detecting hallucination, it may not be universally
effective for all dimensions of text evaluation, even with updated criteria in
prompts. For instance, dimensions such as coherence, which pertains to the
collective quality of all generated sentences, or relevance, which involves
selecting important information and eliminating redundant content from the
reference text, require a holistic focus on the entire candidate. These
dimensions may not be ideally suited for our DCE-AMC approach. However, if a
different evaluator that outputs reasons for action is used, our AMC and RAI
could still be employed to quantify and improve performance on such
dimensions.

Garbage in, Garbage Out. The DCR framework requires two inputs: a reference
paragraph and a candidate paragraph. As we use the reference paragraph as the
target for consistency and hallucination checks, any non-factual statements
present in the reference paragraph would not be detected by our method.
Therefore, for tasks such as retrieval-augmented generation (RAG), the
accuracy of our method is inherently limited by the correctness of the input
paragraphs.

Meta-prompting. Our DCR framework requires hand-craft prompts for specific
tasks, and acknowledges that this is a general hurdle shared by all works
relying on LLMs, which include G-Eval (Liu et al., 2023b), GPTScore (Jinlan et
al., 2023), and Self-refine (Madaan et al., 2023). Specifically, in G-Eval,
different prompts will need to be composed for different aspects: consistency,
coherence, etc. Self-refine defines multiple customized prompts to perform
their INIT - FEEDBACK – REFINE components. Our current solution is to
structure our prompts in a modularized manner so task-specific content can be
updated easily. However, an automated prompt-tuning procedure is beyond the
focus of our study but we leave this for future work.

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##  Appendix A Appendix

###  A.1 Improvement Iterations Example

Table 6: SummEval Improvement Iteration Example Original Article |  Usain Bolt will compete at the IAAF/BTC World Relays in the Bahamas next month , the Jamaica Athletics Administrative Association has announced . The six-time Olympic gold medallist will compete at the relay championship on May 2 and 3 as part of the Jamaican team . ’ I ’m happy to be part of the Jamaican team for the IAAF / BTC World Relays in the Bahamas . I am fit , healthy and ready to run , ’ said Bolt . Usain Bolt has confirmed he will be part of Jamaica ’s team at the World Relays in the Bahamas Bolt reacts as he wins 4x100m gold at the London Olympic Games in 2012 ’ I hear the meet was a lot of fun last year and there was a great atmosphere . Jamaica has a long and successful tradition in relays and when we put on the national colours we always do our best to make the country proud , ’ he added . JAAA General Secretary Garth Gayle commented , ’We were extremely pleased that Usain was available for selection and that the world ’s fastest man will be running for Jamaica . We can expect some sprint magic on the track in the Bahamas on 2nd and 3rd May . ’ …
---|---
Original Summary |  Usain bolt will compete at the relay championship on may 2 and 3 as part of the jamaican team . The six-time olympic gold medalist will be part of jamaica ’s team at the iaaf/btc world . Bolt is the iaaf/btc general secretary garth gayle said he always does ‘ best to make his country proud ’ . …
Divide-Conquer Evaluator Evaluation Reasons |  • This sentence is consistent with the article. The article states that Usain Bolt will compete at the IAAF/BTC World Relays in the Bahamas next month as part of the Jamaican team • This sentence is consistent with the article. The article confirms that Usain Bolt, a six-time Olympic gold medallist, will be part of Jamaica’s team at the IAAF/BTC World Relays • This sentence is not consistent with the article. The article does not state that Bolt is the IAAF/BTC General Secretary. The General Secretary is Garth Gayle. However, the article does mention that Bolt always does his best to make his country proud …
First Round Improved Answer |  Usain bolt will compete at the relay championship on may 2 and 3 as part of the jamaican team. The six-time olympic gold medalist will be part of jamaica ’s team at the iaaf/btc world. Bolt, as stated by the IAAF/BTC General Secretary Garth Gayle, always does his best to make his country proud. …
Divide-Conquer Evaluator Re-Evaluation Reasons |  • This sentence is consistent with the article. The article states that Usain Bolt will compete at the IAAF/BTC World Relays in the Bahamas next month, which is the relay championship mentioned in the summary. The dates and his participation as part of the Jamaican team are also mentioned in the article • This sentence is consistent with the article. The article mentions that Usain Bolt, a six-time Olympic gold medallist, will be part of the Jamaican team at the IAAF/BTC World Relays. • This sentence is not consistent with the article. The article does mention that Bolt always does his best to make his country proud, but it is Bolt himself who says this, not the IAAF/BTC General Secretary Garth Gayle.’ …
Second Round Improved Answer |  Usain bolt will compete at the relay championship on may 2 and 3 as part of the jamaican team. The six-time olympic gold medalist will be part of jamaica ’s team at the iaaf/btc world. Bolt, as stated by himself, always does his best to make his country proud. …

###  A.2 Full sets of Prompts

Table 7: Semantic Consistency Divide-Conquer Evaluator Prompt You are an
evaluator. You will be given a true answer and an attempt answer.
---
The true answer is the ground truth answer. Attempt answer is the answer you
want to evaluate.
Criteria:
Your task is to evaluate whether the attempt answer is consistent with the
true answer. You will evaluate it by:
Listing all the aspects in the attempt answer
Compare if each aspect exists in the true answer
If it does, compare if the information in the attempt answer is consistent
with what is in the true answer
It is OK that not all information from the true answer exists in attempt
answer
Given:
## True Answer ##
{true answer}
## Attempt Answer ##
{answer to evaluate}
Task
Work in a step-by-step way to make sure we get the right answer. You will
format the output in JSON as follows:
{"reason": [{"sentence": "original sentence", "reason": "why this sentence is
or is not consistent with the true answer"}], "is_consistent": true/false}
Here is the evaluation in JSON format:
Table 8: Summarization Consistency Divide-Conquer Evaluator Prompt You are an
evaluator. You will be given an article and a summary.
---
The summary contains a summarized version of the article.
Criteria:
Your task is to evaluate whether the summary is consistent with the article.
You will evaluate it by going through each sentence of the summary and check
against the following procedures:
Understands all the aspects in the sentence, who is doing what at when and
where and what are the impact etc.
Compare if each aspect exists in the article
If it does, compare if the information in this sentence is consistent with
what is in the article
Compare if all the information in this sentence can be directly inferred or
entailed from what is in the article, including but not limited to who, what,
when, where, etc.
It is OK that not all information from the article exists in this summary
Given:
## Article ##
{article}
## Summary ##
{summary}
Task
Work in a step-by-step way to make sure we get the right answer. You will
format the output in JSON as follows:
{"reason": [{"sentence": "original sentence", "reason": "why this sentence is
or is not consistent with the article. You should start with ẗhis sentence is
consistent with the articleör ẗhis sentence is not consistent with the
article"̈}], "is_consistent": true/false}
Here is the evaluation in JSON format:
Table 9: Auto-Metric Converter Prompt You are an evaluator. You will be given
a list of paragraphs about "attempt answer". Your job is to:
---
Identify whether each paragraph is positive or negative
If the paragraph is positive, mark it as 1,
If the paragraph is negative, mark it as -1.
Output the mark for each paragraph in a JSON array
# Example
Given paragraphs:
"The attempt answer is incorrect as it states that employees in the US are not
eligible to participate in the ESPP, which contradicts the true answer. So it
is incorrect",
"The attempt answer adds a new aspect that is not in the true answer.",
"Yet it does list the correct article. And that is helpful."
Thought:
The first paragraph is negative as it mentions the attempt answer is wrong.
Thus mark -1
The second paragraph is negative as it adds something that is not in true
answer. Thus mark -1
The third paragraph is positive. Thus mark 1
Answer:
{"reason": ["The first paragraph is negative as it mentions the attempt answer
is wrong. Thus mark -1", "The second paragraph is negative as it adds
something that is not in the true answer. Thus mark -1", "The third paragraph
is positive. Thus mark +1"], "answer": [ -1, -1, 1]}
Given:
## Attempt Answer ##:
{attempt answer}
Answer:
Table 10: Reason-Assisted Improver Prompt You are a good writer. You will be
given:
---
An article
A list of objects, each have two fields: sentence and reason
** sentence: These sentences are summaries of the given article.
** reason: These are the reasons why the sentence is consistent with the
article or not.
Your job is to rewrite these sentences:
If the sentence is consistent with the article, you can keep it as it is
If the sentence is not consistent with the article, you can re-write it to
make it consistent with the article based on the reasons given.
Article
{article}
Sentences
{sentences}
Task
Work in a step-by-step way to make sure we get the right answer. You will
format the output in JSON as follows:
[ {"sentence": "original sentence", "improved_sentence": "improved sentence",
"reason": "if it is improved, how it is improved. if not, say ’ALREADY
CONSISTENT’"}]
Table 11: Paragraph Level Evaluator Prompt You are an evaluator. You will be
given an article and a summary.
---
The summary contains a summarized version of the article.
Criteria:
Your task is to evaluate whether the summary is consistent with the article.
You will evaluate it by going through the summary and checking against the
following procedures:
Understands all the aspects in the summary, who is doing what at when and
where and what are the impact etc.
Compare if each aspect exists in the article
If it does, compare if the information in this summary is consistent with what
is in the article
Compare if all the information in this summary can be directly inferred or
entailed from what is in the article, including but not limited to who, what,
when, where, etc.
It is OK that not all information from the article exists in this summary
Given:
## Article ##
{article}
## Summary ##
{summary}
Task
Work in a step-by-step way to make sure we get the right answer. You will
format the output in JSON as follows:
{"reason": {"sentence": "original sentence", "reason": "why this summary is or
is not consistent with the article. You should start with T̈his summary is
consistent with the articleör ẗhis summary is not consistent with the
article"̈}, "is_consistent": true/false}
Here is the evaluation in JSON format:
Table 12: Paragraph Level Reason Assisted Improver Prompt You are a good
writer. You will be given:
---
an article
a summary of the article
a reason listing why the summary is or is not consistent with the article
Your job is to rewrite this summary:
if the summary is consistent with the article, you can keep it as it is
if the summary is not consistent with the article, you can rewrite it to make
it consistent with the article based on the reasons given.
Article
{article}
Summary
{summary}
Reason
{reason}
Task
Work in a step-by-step way to make sure we get the right answer. You will
format the output in JSON as follows:
{"sentence": "original summary", "improved_summary": "improved summary",
"reason": "if it is improved, how it is improved. if not, say ’ALREADY
CONSISTENT’"}

###  A.3 Divide-Conquer Evaluator Examples

Table 13: QQP Evaluation Example True Answer |  Attempt Answer |  Is_Consistent |  Reasons
---|---|---|---
How do I read and find my YouTube comments? |  How can I see all my YouTube comments? |  True |  The sentence is asking the same question as the true answer, just phrased differently. Both are asking about how to view one’s own comments on YouTube
What are the laws to change your status from a student visa to a green card in the US, how do they compare to the immigration laws in Canada? |  What are the laws to change your status from a student visa to a green card in the US? How do they compare to the immigration laws in Japan? |  False |  • This sentence is consistent with the true answer as it asks about the laws to change status from a student visa to a green card in the US. • This sentence is not consistent with the true answer. The true answer asks for a comparison with the immigration laws in Canada, not Japan."
If I fire a bullet backward from an aircraft going faster than the bullet; will the bullet be going backward? |  Do bullets travel faster than the speed of sound when shot from a gun? If not, is it possible? If they do, what gun and how much devastation occurs? |  False |  • This sentence is not consistent with the true answer because it is asking about the speed of bullets in relation to the speed of sound, not about the direction of a bullet when fired from a fast-moving aircraft. • This sentence is not consistent with the true answer because it is asking about the possibility of bullets traveling faster than the speed of sound, not about the direction of a bullet when fired from a fast-moving aircraft. • This sentence is not consistent with the true answer because it is asking about the type of gun and the amount of devastation, not about the direction of a bullet when fired from a fast-moving aircraft.
Table 14: PAWS Evaluation Example True Answer |  Attempt Answer |  Is_Consistent |  Reasons
---|---|---|---
What were the major effects of the cambodia earthquake , and how do these effects compare to the Iquique earthquake in 1877 ? |  What were the major effects of the Iquique earthquake , and how do these effects compare to the cambodia earthquake in 1877 ? |  False |  The sentence is not consistent with the true answer because it reverses the order of the earthquakes. The true answer asks about the effects of the Cambodia earthquake and compares it to the Iquique earthquake in 1877, while the attempt answer asks about the effects of the Iquique earthquake and compares it to the Cambodia earthquake in 1877.
How do I start learning electronic music production ? Should I begin with books or videos ? I have logic pro 9 and I already made some songs but I would like to start from scratch ( as if I did not know anything ) . |  How do I begin learning electronic music production ? Should I start with books or videos ? I have logic pro 9 and I already made some songs but I would like to start from scratch ( as if I did not know anything ) . |  True |  • This sentence is consistent with the true answer. The only difference is the use of the word ’begin’ instead of ’start’, but they mean the same thing. • This sentence is consistent with the true answer. It is exactly the same. • This sentence is consistent with the true answer. It is exactly the same.
Table 15: SummEval Evaluation Example Original Article |  Paul Merson has restarted his row with Andros Townsend after the Tottenham midfielder was brought on with only seven minutes remaining in his team ’s 0-0 draw with Burnley on Sunday . ’Just been watching the game , did you miss the coach ? # RubberDub # 7minutes , ’ Merson put on Twitter . Merson initially angered Townsend for writing in his Sky Sports column that ’if Andros Townsend can get in ( the England team ) then it opens it up to anybody . ’ Paul Merson had another dig at Andros Townsend after his appearance for Tottenham against Burnley Townsend was brought on in the 83rd minute for Tottenham as they drew 0-0 against Burnley Andros Townsend scores England ’s equaliser in their 1-1 friendly draw with Italy in Turin on Tuesday night The former Arsenal man was proven wrong when Townsend hit a stunning equaliser for England against Italy and he duly admitted his mistake . ’It ’s not as though I was watching hoping he would n’t score for England , I ’m genuinely pleased for him and fair play to him ‚Äì it was a great goal , ’ Merson said . ’It ’s just a matter of opinion , and my opinion was that he got pulled off after half an hour at Manchester United in front of Roy Hodgson , so he should n’t have been in the squad . ’When I ’m wrong , I hold my hands up . I do n’t have a problem with doing that - I ’ll always be the first to admit when I ’m wrong . ’ Townsend hit back at Merson on Twitter after scoring for England against Italy Sky Sports pundit Merson ( centre ) criticised Townsend ’s call-up to the England squad last week Townsend hit back at Merson after netting for England in Turin on Wednesday , saying ’Not bad for a player that should be ’nowhere near the squad ’ ay @ PaulMerse ? ’ Any bad feeling between the pair seemed to have passed but Merson was unable to resist having another dig at Townsend after Tottenham drew at Turf Moor .
---|---
Original Summary Divided into Bullet Points |  • Paul merson was brought on with only seven minutes remaining in his team ’s 0-0 draw with burnley. • Andros townsend scored the tottenham midfielder in the 89th minute . • Paul merson had another dig at andros townsend after his appearance . • The midfielder had been brought on to the england squad last week . • Click here for all the latest arsenal news news .
Reasons |  • This sentence is not consistent with the article because it was Andros Townsend, not Paul Merson, who was brought on with only seven minutes remaining in the game. • This sentence is not consistent with the article because it does not mention that Andros Townsend scored in the 89th minute. Also, the phrase ’scored the Tottenham midfielder’ is not clear. • This sentence is consistent with the article. The article mentions that Paul Merson had another dig at Andros Townsend after his appearance. • This sentence is not consistent with the article because it does not mention that Andros Townsend was brought onto the England squad last week. • This sentence is not consistent with the article because the article does not provide any latest Arsenal news.
Is_Consistent |  False
Table 16: QAGS-XSUM Evaluation Example Original Article |  London ’s first history day will be held on the anniversary of big ben ’s first day in operation . It will be first celebrated on 31 may in 2017 with celebrations and events run by historic england . The date was decided upon after a poll involving 1,000 londoners . It was closely followed by 5 september - the date of the great fire of london . The yougov questionnaire also declared the houses of parliament as the building that best sums up london . People voted for the queen as their favourite historic london hero for the moment she secretly joined the crowds to celebrate victory in europe day . The results of the poll were released to mark the launch of historic england ’s “ keep it london ” campaign . People were asked to select a date to celebrate the capital ’s history , their historic hero and the building that sums up london . Big ben ’s first day in operation was 31 may 1859 . The campaign is intended to encourage londoners to notice , celebrate and speak up for the heritage of their city , historic england said . The public body has also launched a film entitled i am london , which celebrates the historic buildings and places that have borne witness to the capital ’s history . Duncan wilson , chief executive of historic england , said : “ our heritage is a source of pride , a reminder of the city ’s past , a foundation for its present and the building blocks for its future .
---|---
Original Summary Divided into Bullet Points |  • Big ben ’s 150th anniversary has been chosen as the date to celebrate london ’s history .
Reasons |  • This sentence is not consistent with the article. The article states that London’s first history day will be held on the anniversary of Big Ben’s first day in operation, which is 31 May 1859. However, it does not mention that this date is the 150th anniversary of Big Ben.
Is_Consistent |  False
Table 17: QAGS-CNN Evaluation Example Original Article |  A southern iowa chiropractor accused of accepting sex as payment for his services and performing exorcisms on patients has surrendered his state license . The iowa board of chiropractic released a report wednesday detailing charges against charles manuel , of lamoni . Manuel signed an agreement last month admitting his misdeeds and pledging not to apply for reinstatement for at least 10 years . Patient satisfaction : a chiropractor in iowa has surrendered his license to practice and admitted to swapping services for sex and performing exorcisms on some patients . Nonetheless , he ’s received outstanding evaluations for patient satisfaction on healthgrades . Com . The agreement requires that manuel prove any circumstances surrounding the revocation of his license no longer exist before he can resume practicing chiropractic in the state . Those circumstances included bartering sex for services with some patients . Manuel also recommended that patients stop taking medication he prescribed to them . A woman who answered a call to manuel ’s home from the des moines register declined to comment on the case . A woman at his former practice said he had n’t worked there for some time . A lamoni address listed on the yelp page for manuel ’s practice appears to be a home on a residential street . While maneul has received just three patient survey responses on healthgrades . Com , those responses were quite positive . The disgraced chiropractor received a perfect five out of five stars in patient satisfaction . Strange practice : charles manuel , who admitted wrongdoing to the iowa board of chiropractic , listed his practice ’s addresses on this residential street in the small agricultural town of lamoni in southern iowa .
---|---
Original Summary Divided into Bullet Points |  • A chiropractor in iowa has surrendered his license to practice and admitted to swapping services for sex and performing exorcisms on some patients. • Manuel also recommended that patients stop taking medication no longer exist before he can resume practicing chiropractic in the state . • The disgraced chiropractor received a perfect five out of five stars in patient satisfaction .
Reasons |  • This sentence is consistent with the article. The article states that Charles Manuel, a chiropractor in Iowa, surrendered his license and admitted to accepting sex as payment for his services and performing exorcisms on patients. • This sentence is not consistent with the article. The article does mention that Manuel recommended patients stop taking medication he prescribed to them, but it does not state that this condition must be met before he can resume practicing. • This sentence is consistent with the article. The article mentions that despite his misdeeds, Manuel received outstanding evaluations for patient satisfaction on healthgrades.com, including a perfect five out of five stars.’]
Is_Consistent |  False

###  A.4 Example of setting $\alpha$ and $\beta$ in equation 3

$\alpha$ and $\beta$ are added to ensure we are using a fully sentence level
analysis. Below we give an example on how to use them to achieve this goal.

Suppose the output from Divide-Conquer Evaluator with customized prompt is:

⬇

1{

2 "is_consistent" : False,

3 "reasons" : [

4 "The two paragraphs are not consistent.",

5 "This sentence is consistent."

6 "This sentence is not consistent."

7 ]

8}

After inspecting the reasons we notice that the first entry is not a sentence
level analysis, but a paragraph level one. Thus when calculating scores we
should remove the impact of this line.

Since in this case the first entry is negative, it will be given -1 score,
thus we should set $\alpha=1$ to mitigate this effect. Similarly, because we
only have one entry that is not sentence level, we would set $\beta=-1$.

Thus for this particular case, equation 3 will be:

$\mathcal{Z}=\left(\sum_{i=1}^{k}z_{i}+1\right)/(k-1),\quad\hat{\mathcal{Z}}=(\mathcal{Z}+1)/2,\quad\hat{\mathcal{Z}}\in[0,1]$
(5)

###  A.5 What does LLM think of sentence level approach?

To have some more fun, we asked different LLMs on what they think the best
approach to evaluate consistency is, token level, paragraph level, or sentence
by sentence level. We have a unanimous decision :)

Table 18: LLM’s opinion on our sentence level approach Input Prompt |  Given a reference, we have a generated paragraph from LLM. Our objective is to check whether the generated text is consistent to the reference or not. Can you you tell me, to achieve the best accuracy, would you choose to compare them token-by-token, direct check of paragraphs as a whole, or sentence by sentence check on the semantic meaning?
---|---
GPT 3.5 |  To achieve the best accuracy in checking the consistency of generated text with a reference, it is recommended to compare them sentence by sentence on the semantic meaning. This approach allows for a more nuanced analysis of the generated text and can identify inconsistencies that may not be apparent when comparing them token-by-token or as a whole paragraph. Additionally, comparing them sentence by sentence allows for a more efficient and targeted review of the generated text, as inconsistencies can be identified and addressed on a sentence-by-sentence basis.
GPT 4 |  To achieve the best accuracy, it would be most effective to check sentence by sentence on the semantic meaning. This is because language can be complex and nuanced, and a direct token-by-token or whole paragraph comparison might miss subtle differences in meaning. By checking sentence by sentence, you can ensure that the generated text accurately reflects the reference in both content and context.
PaLM2 |  To achieve the best accuracy in checking whether a generated paragraph from LLM is consistent with a reference, it would be best to compare them sentence by sentence on the semantic meaning. This is because comparing token-by-token would be too time-consuming and would not take into account the overall meaning of the sentences. Direct check of paragraphs as a whole would also not be as effective, as it would not identify specific inconsistencies between the sentences. Comparing them sentence by sentence on the semantic meaning would allow for a more thorough and accurate check of the consistency between the generated paragraph and the reference.
Claude 2 |  Overall, I would recommend the sentence-by-sentence semantic comparison approach. By checking the semantic consistency at a sentence level, it provides a good balance between catching inconsistencies in meaning while allowing for minor paraphrasing and word order changes. The paragraph-level comparison is too holistic while the token-by-token approach is too strict. Focusing on sentence-level meaning comparisons allows catching semantic inconsistencies with reasonably high accuracy.