Venue: NAACL 2024

# Less is More for Improving Automatic Evaluation of Factual Consistency

 Tong Wang, Ninad Kulkarni, Yanjun Qi

AWS Bedrock Science

{tonwmg, ninadkul, yanjunqi}@amazon.com

###### Abstract

Assessing the factual consistency of automatically generated texts in relation to source context is crucial for developing reliable natural language generation applications. Recent literature proposes AlignScore which uses a unified alignment model to evaluate factual consistency and substantially outperforms previous methods across many benchmark tasks. In this paper, we take a closer look of datasets used in AlignScore and uncover an unexpected finding: utilizing a smaller number of data points can actually improve performance. We process the original AlignScore training dataset to remove noise, augment with robustness-enhanced samples, and utilize a subset comprising 10% of the data to train an improved factual consistency evaluation model, we call LIM-RA (Less Is More for Robust AlignScore). LIM-RA demonstrates superior performance, consistently outperforming AlignScore and other strong baselines like ChatGPT across four benchmarks (two utilizing traditional natural language generation datasets and two focused on large language model outputs). Our experiments show that LIM-RA achieves the highest score on 24 of the 33 test datasets, while staying competitive on the rest, establishing the new state-of-the-art benchmarks.

## 1 Introduction

The emergence of large language models (LLMs) and an increasing interest in utilizing machine-generated texts from like summarization, paraphrasing, and question-answering (QA) has created a need to automatically evaluate the degree to which generated natural language texts accurately reflect the factual information contained in source context. Early work used Natural Language Inference (NLI) Laban et al. (2022) and QA Fabbri et al. (2021) to handle automatic factual consistency evaluation. However, these methods exhibit limited generalizability and struggle with handling long contexts. Recently, Zha et al. (2023) propose AlignScore, a unified model based on RoBERTa and is trained on a wide range of datasets to calculate the alignment between context and generated text. AlignScore achieves state-of-the-art results across several factual consistency benchmarks.

Despite its strengths, the AlignScore study has several limitations. First, the training data leveraged for developing AlignScore were derived in a heuristic manner from many existing NLP tasks and datasets, adding noise and poor quality in some samples. We, therefore, ask the question: Are all data points from AlignScore training needed? Our ablation studies shown in Figure 1 indicate that the answer is "No". Additionally, AlignScore displays fragility regarding robustness, as it fails to identify some clear perturbations involving entities like names, numbers, etc. As Table 1 illustrates, even simple modifications can produce false positives and false negatives when using AlignScore.

In this paper, we propose **LIM-RA** (**Less Is More** - **R**obust **AlignScore**), an improved version

Figure 1: Ablation study on using the first \(n\) samples from each sub-train dataset for training and overall model performance. We see that the optimum benchmark performance is 452K and 1.6M samples for DeBERTa and RoBERTa respectively. For comparison AlignScore uses 4.7M or the first 500K. Performance broken down by benchmark can be found in A.1

of AlignScore trained on DeBERTa He et al. (2021). Our model is the result of multiple ablation steps on improving training data quality, analyzing training size as well as constructing synthetic data to improve robustness (Figure 2 shows overall workflow). We demonstrate that with about 10% of the cleaned training data, we are able to obtain a better model than AlignScore. Our experiments show that LIM-RA consistently outperforms strong baselines including AlignScore and GPT-3.5-Turbo, achieving the new state-of-the-art on four factual consistency benchmarks covering a wide range of 33 datasets. It is worth noting that our experiments include a newly defined benchmark, Large Language Model Response (LLMR), designed for evaluating LLM outputs' factual consistency. LIM-RA performs the best on LLMR.

## 2 Method

### AlignScore Model and Training Data

Automatic evaluation of factual consistency is challenging. Recently proposed AlignScore measures the alignment of information between machine-generated natural language texts and given source material to evaluate the factual accuracy Zha et al. (2023). AlignScore is built on top of a unified alignment function via RoBERTa Liu et al. (2019) and trained on datasets derived from 7 NLP tasks: NLI, QA, Fact Verification, Paraphrase, Semantic Textuality Similarity, Information Retrieval, and Summarization. Each sample in a task is converted into a text pair (context, claim) and a label. The label has 3 options based on the task and dataset: binary (aligned, not-aligned), 3-way (aligned, contradict, neutral), regression (score between 0 to 1). For example in SNLI dataset, the context is the premise, the claim is the hypothesis, label is the 3-way label. Certain prepossessing steps are required to unify the format in multiple datasets.

To calculate the factual consistency score of long text, AlignScore first splits the context into roughly 350-token chunks and the claim into sentences. Then the trained alignment function (RoBERTa based) evaluates each sentence in the claim against each context chunk. For example, in the 3-way classification head, the probability of the "aligned" class is used as the alignment score. The highest alignment score for each claim sentence is selected and then averaged to obtain the overall factual consistency score. By using the chunking strategy, AlignScore can be applied to text of any length, as shown by Figure 3.

### Training Data Cleaning

For training, AlignScore uses more than 30 datasets and selects 500K samples from each dataset to build its trainng data, including a total of 4.7M training samples. Training the AlignScore alignment model requires 5 days on 8 V100 GPUs.

However, we find that not all the training datasets have good quality. The upper half of Figure 2 shows a cohort of data cleaning steps we use to improve the training data quality. First, based on our ablation studies, we remove four datasets that do not result in performance gains, such as ms_marco and wikihow. Additionally to prevent the model from truncating sentences that support the claim, we only keep samples in which the context has fewer than 512 tokens.

When using QA datasets to create alignment training samples, since the QA passage is the context, a preprocessing step is needed. AlignScore uses a pre-trained sequence-to-sequence model to convert question-answer into a declarative sentence as the input claim. We, however, observed a performance decrease in our experiments when using this preprocessing. We find the decrease was because the generated declarative sentence has poor data quality. Thus, we concatenate question and answer as the claim text. 1

Footnote 1: We also tried to use Mistral-7B Jiang et al. (2023) fewshot to generate better-quality declarative sentences but still did not produce performance gains.

Additionally, many QA datasets only have

\begin{table}
\begin{tabular}{l|p{65.4pt}|c|c|c} \hline Context & Claim & AlignScore & LIM–RA \\ \hline [...] Napoleon married the Archduchess Maric Louise was 18 years old when she married Napoleon & 0.9907 & 0.9542 \\ who was 18 years old [...] & Archduchess Mari Louise was 18 years old when she married Napoleon & 0.9650 & 0.4381 \\ \end{tabular} \begin{tabular}{l|p{65.4pt}|c|c} \hline The Blue Ridge Mountains [...] attain elevations of about 2,000 ft. & The typical elevations of the Blue Ridge Mountains are 2,000 ft. & 0.9812 & 0.9434 \\ \end{tabular} 
\begin{tabular}{l|p{65.4pt}|c|c} \hline The Blue Ridge Mountains [...] attain elevations of about 2,000 ft. & The typical elevations of the Blue Ridge Mountains are 2000 ft. & 0.0214 & 0.8621 \\ \end{tabular}
\end{table}
Table 1: Examples of robustness issues in AlignScore predictions. In the first example we perturb the correct name “Marie Louise” to the incorrect name ”Mari Louze”; however, the factual consistency score is still high, resulting in a false positive. Similarly in the second example we perturb ”2,000” to ”2000”, resulting in a false negative.

 

ground truth answers (positive samples) but no wrong answers (negative samples). To address this, AlignScore generates fake wrong answers using the T5 model, and answers the question based on the original passage with the ground truth answer tokens masked. However, this leads to false negatives because many generated fake answers are similar to or exactly match their corresponding ground truth answers. To mitigate the issue, we use Sentence-BERT Reimers and Gurevych (2019) to encode both the fake and ground truth answers, and then filter out the fake answers that are similar to the true answers by using rules and a threshold of 0.85. This data cleaning procedure is illustrated in the top half of figure 2.

After cleaning the data, we use 20K samples from each dataset for a total of 452K training samples (about 10% of training data used for AlignScore) which results in a better model (results in Section 3.3).

### Synthetic Robustness Data

We also notice AlignScore fails on name or number perturbations as illustrated in Table 1. To mitigate the issue, we augment the training dataset by creating a synthetic dataset designed to enhance the model's robustness, with emphasis on name and number variation based text generation as illustrated in the bottom half of figure 2.

We create two synthetic datasets: Robust-Name and Robust-Number datasets using DocNLI Yin et al. (2021). DocNLI includes multiple-sentence contexts and single-sentence claims discussing facts in the context. To create the Robust-Name data, we use spaCy NER Honnibal and Montani (2017) to identify the "PERSON" and "ORG" entities in samples labeled as "entailment" and use Mistral-7B to perturb the entities (prompt details in Appendix A.3). The original entity is replaced with the perturbed entity to construct the synthetic negative samples. Using Mistral instead of randomly perturbing a character in the entity ensures the new name is similar to a real person or org name. The two-step generation generates a better rewritten claim than directly instructing the LLM to rewrite the claim.

Similarly, we construct the Robust-Number data by perturbing claims with number-related labels such as "TIME", "QUANTITY", "DATE". We use Mistral to rephrase ("100" to "one hundred") and change numbers ("100" to "101"). The perturbed entities replace the original to create positive and negative data.

### LIM-RA Model

We experiment with different pretrained models as base including RoBERTa (large), DeBERTa (large), DistilBERT (base). DeBERTa achieves the best overall performance while DistilBERT has poor performance due to its small model capacity. Also, we unify all data labels to the three class setup (details later in this section ), and use the 3-way classification head to predict \(aligned\) (factual consistent), \(neutral\) (no-evidence), and \(contradiction\). At inference time, we follow AlignScore to split context into chunks and claim into sentences, and average the sentence alignment scores to compute the overall factual consistency score. We denote LIM-RA and LIM-A as the DeBERTa model trained with cleaned data and with and without synthetic robustness in training, respectively.

**Under the Hood:** We train a pre-trained NLI DEERta model2Laurer et al. (2024) for 3 epochs using AdamW optimizer with learning rate as 1e-5.

Figure 2: Overall workflow of our method is depicted in the diagram. The top workflow describes how we clean the training data, the bottom workflow illustrates the process of creating synthetic robustness data. Then we train a pre-trained DeBERTa model on those data to obtain LIM-RA.

 

We use the first 20k samples from each of the 28 train datasets described in AlignScore, plus the 2 new synthetic robustness datasets, resulting in a total of 490k samples in our final training. Hyper-parameter details can be found in Table 10. We follow AlignScore and use the factual consistency class probability as the alignment score.

Unifying Labels:We convert binary and regression labels to 3-class labels. For datasets with binary labels, we map the negative label "not-aligned" to either "contradiction" or "no-evidence" depend on the dataset. In most of the cases, we map the negative label to "contradiction", such as in _doc_nli_ and _paws_. But in _qqp_, we map the negative label to "no-evidence". For regression labels in _stsb_ dataset, we bin the score as three classes: faithful (\(>=0.45\)), no-evidence (\(>=0.3,<0.45\)), contradiction (\(<0.3\)).

### Connecting to Related Works

Previous studies include multiple other methods for assessing factual consistency. (1) QA-based factual consistency, including QuestEval Scialom et al. (2021) and QAFactEval Fabbri et al. (2021), checks if the source answer is different from the target answer given a question. (2) With the recent advances in LLMs, a new line of research is to evaluate factual consistency directly with an LLM Liu et al. (2023); Jia et al. (2023). Chen et al. (2023) investigate a variety of prompting methods including vanilla prompting, chain-of-thought prompting, and a sentence-by-sentence prompting and Luo et al. (2023) explore ChatGPT's ability to evaluate factual inconsistency under a zero-shot setting while Fu et al. (2023) uses LLMs in a QA setting for direct factual consistency scoring. (3) A third related line of methods uses the Natural Language Inference (NLI) based formulation. For instance Laban et al. (2022) proposed SummaCConv, that segments documents into sentences and aggregates NLI scores between pairs of sentences.

Factual consistency benchmark datasets typically contain (context, claim, label) triplets where the label indicates if the claim is consistent with the context and is difficult to obtain as high-quality annotation is challenging due to low inter-annotator agreement Falke et al. (2019); Laban et al. (2022). Laban et al. (2022) introduce the SummaC (Summary Consistency) benchmark which consists of 6 large inconsistency detection datasets standardized as a binary classification task given document and summary. Laban et al. (2023) introduce SummEdits, a summarization consistency dataset where an LLM introduces inconsistencies in an otherwise consistent summary and show that the benchmark is challenging for most current LLMs. Honovich et al. (2022) present TRUE, which consolidates 11 existing datasets covering summarization, knowledge-grounded dialogue, paraphrasing and fact verification annotated for consistency.

## 3 Experiments

We conduct a comprehensive experimental study to evaluate LIM-RA on multiple factual consistency benchmarks and demonstrate LIM-RA consistently outperforms strong baselines and establishes new state-of-the-art results. Our experiments also include ablation studies (Table 7) and robustness analysis (Table 9) of LIM-RA. We list the hyperparameters we used for LIM-RA in Table 10. Each of our experiments covers 20 different random seeds.

### Four Benchmarks: 33 Datasets

We evaluate the factual consistency performance using AUC-ROC on 33 datasets from 4 benchmarks: SummaC, SummEdits, TRUE, and LLMR. Each data sample in the benchmarks is a pair of target text (claim) and a grounding source text (context), with a binary annotation of whether the target text is actually consistent w.r.t its source. The benchmark dataset details can be found in Appendix A.2.

**SummaC** 5 summary consistency datasets: GoGenSumm (CG), XsumFaith (XF), FactCC (FC), SummEval (SE), Frank (FRK). We remove Polytope dataset since it contains negative samples that do not imply factual consistency errors.

**TRUE** 11 datasets covering summarization, knowledge-grounded dialogue, paraphrasing and fact verification annotated for factual consistency: Frank (FRK), SummEval (SE), MNBM, QAGS

\begin{table}
\begin{tabular}{c c c c c c c} \hline Model & CG & XF & FC & SE & FRK & AVG \\ \hline NER & 54.4 & 69.0 & 50.8 & 59.3 & 68.4 & 60.4 \\ Questavel & 59.7 & 65.6 & 73.3 & 76.9 & 86.3 & 72.4 \\ QAFactEval & 82.5 & 65.1 & 89.2 & 88.5 & 89.6 & 82.9 \\ SummaC & 65.6 & 70.3 & 92.2 & 86.0 & 88.4 & 80.5 \\ AlignScore & 76.9 & **78.1** & 89.1 & 82.3 & 88.0 & 82.9 \\ LIM-A & 84.2 & 73.9 & **93.7** & **92.4** & 90.3 & 86.0 \\ LIM-RA & **84.9** & 75.7 & 93.2 & 92.2 & **92.0** & **87.6** \\ \hline \end{tabular}
\end{table}
Table 2: SummaC benchmark AUC-ROC results. LIM-RA and LIM-A outperform all current baselines in 4 of the 5 datasets with LIM-RA performing the best overall.

 

CNNDM (QC), QAGS-Xsum (QX), BEGIN, Q\({}^{2}\), DialFact (DF), Fever (FVR), VitaminC (VITC), PAWS.

**SummEdits** 10 datasets evaluating factual consistency in summarization covering multiple domains. Inconsistent summaries are generated by GPT-3.5-Turbo: News, Podcast (PD), Billsum (BILL), Samsum (SS), Shakespeare (SP), SciTLDR (SCI), QMSum (QM), ECTSum (ECT), Sales Email (SEmail), Sales Call (SCall).

**LLMR** (large language model response) is a new benchmark consisting of 7 datasets we introduce in this paper. Similar to SummEdits, the datasets are designed to evaluate the factual consistency of LLM output and inconsistencies are generated in an automated fashion with human verification: HaluEval (HE) Li et al. (2023) consists of CNN/DailyMail articles with correct and hallucinated summaries generated by ChatGPT in a zero-shot manner. BAMBOO abs-hulu (BBA) and sen-hallu (BBS) subsets Dong et al. (2023) consist of NLP academic papers (max 4K and 16K token variants for a total of 4 datasets) with supported and hallucinated hypotheses generated by ChatGPT similar to HE. Passage-level Hallucination Detection (PHD) Yang et al. (2023) consists of Wikipedia articles of an entity with correct and hallucinated biographies of that entity generated by ChatGPT. AttrScore (ATS) Yue et al. (2023) consists of QA datasets and New Bing search queries in the format \((question,answer,context,label)\) where \(label\) indicates if the \(answer\) is supported by \(context\). Hallucinations are generated by both swapping the answer with an incorrect answer and by swapping the the context with another article. For our experiments we consider context as \(document\) and answer as \(claim\).

### Baselines Methods

NERLaban et al. (2022), uses spaCy NER to match entities between claim and context.

**Questeval**, QA-based model, evaluates both factual consistency and relevance of the generated text by checking if the answer from source is different from the answer from target given a question.

**QAFactEval**, QA-based model, evaluates factual consistency by performing answer selection, question generation, question answering, and answer

\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline Model & ETS & BBA-A & BRA-16 & BBS-A & BBS-16 & PHD & HE & AVG \\ \hline AlignScore & 62.7 & 62.4 & 59.4 & 71.8 & 75.2 & 74.6 & 73.1 & 68.5 \\ LIM-A & 65.9 & 69.2 & 60.4 & 79.5 & 78.4 & **78.0** & 72.2 & 71.9 \\ LIM-RA & **66.3** & **71.4** & **60.7** & **82.5** & **79.6** & 77.0 & **74.9** & **73.2** \\ \hline \end{tabular}
\end{table}
Table 4: LLMR benchmark AUC-ROC results. We compare against only AlignScore as it is the best performing baseline as seen in tables 2 and 3. LIM-RA and LIM-A are the best performing models in all 7 datasets. LIM-RA is the best performing model overall.

\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline Model & ECT & QM & SCall & SS & SCI & SEmail & NEWS & BILL & PD & SP & AVG \\ \hline NER & 59.7 & 55.2 & 56.3 & 57.6 & 53.3 & 66.8 & 60.9 & 49.1 & 57.8 & 51.5 & 56.8 \\ Questeval & 64.8 & 54.0 & 63.1 & 54.4 & 51.9 & 55.9 & 64.9 & 59.8 & 54.1 & 53.4 & 57.6 \\ QAFacEval & 75.8 & 65.5 & 74.6 & 71.3 & 69.7 & 69.8 & 81.4 & 56.9 & 64.0 & 65.5 & 69.4 \\ SummaC & 66.7 & 55.8 & 61.1 & 54.3 & 61.0 & 58.9 & 61.1 & 54.5 & 61.0 & 61.1 & 59.6 \\ AlignScore & 91.5 & 83.8 & 89.1 & **85.5** & 82.1 & 81.6 & 80.6 & 61.6 & 78.0 & 72.3 & 80.6 \\ LIM-A & **93.6** & 86.9 & 90.7 & 83.1 & **87.7** & **82.5** & **82.1** & 69.3 & 81.0 & 86.9 & 84.4 \\ LIM-RA & 92.8 & **88.2** & **91.2** & 84.2 & 86.0 & 81.1 & 81.3 & **72.1** & **82.5** & **86.9** & **84.6** \\ \hline \end{tabular}
\end{table}
Table 3: SummEdits benchmark AUC-ROC results. LIM-RA and LIM-A are the best performing models in 9 of the 10 datasets and LIM-RA is the best performing model overall.

\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline Model & ETS & BMA-A & BRA-16 & BBS-A & BBS-16 & PHD & HE & AVG \\ \hline AlignScore & 62.7 & 62.4 & 59.4 & 71.8 & 75.2 & 74.6 & 73.1 & 68.5 \\ LIM-A & 65.9 & 69.2 & 60.4 & 79.5 & 78.4 & **78.0** & 72.2 & 71.9 \\ LIM-RA & **66.3** & **71.4** & **60.7** & **82.5** & **79.6** & 77.0 & **74.9** & **73.2** \\ \hline \end{tabular}
\end{table}
Table 5: TRUE benchmark AUC-ROC results. LIM-RA and LIM-A are the best performing models in 6 of the 11 datasets. LIM-RA is the best performing model overall. We report AVG\({}^{*}\) in the second last column by excluding PAWS, FVR, and VITC to show out-of-domain performance.

 

overlap evaluation.

**SummaC**, NLI-based model (SummaCConv), segments documents into sentence units and aggregates scores between pairs of sentences.

**AlignScore**, current state-of-the-art, an alignment function trained on a wide range of datasets.

**0-shot/10-shot GPT-3.5-Turbo**, instruct the LLM to evaluate whether the claim is consistent, lacks evidence, or contains contradictions.

**10-shot Mistral-7B**, one of the best performing open-source LLMs. We use the same prompts as 10-shot GPT-3.5-Turbo.

### Experimental Results

#### 3.3.1 Results on Traditional Benchmarks: SummaC and TRUE

We evaluate factual consistency models on the SummaC benchmark in Table 2. LIM-RA achieves the best overall score and has a 5.7% relative improvement over AlignScore and QAFactEval. Our model has the top result in 4 of the 5 datasets. Our results for AlignScore are lower than the results reported in the original work [6] because we did not include the rule-based inference-time processing (such as removing special tokens or capitalizing the first letter) for a fair comparison between all models.

From the results on the TRUE benchmark in Table 5, we see that LIM-RA has the best overall AUC-ROC score with a 0.9% improvement over AlignScore and has the best score in 5 of 11 datasets. As suggested in [6], we report AVG\({}^{*}\) by removing PAWS, FVR, and VITC to show out-of-domain performance; LIM-RA remains the best performing model.

#### 3.3.2 Results on LLM output: SummEdits and LLMR

We evaluate factual consistency on LLM responses using the SummEdits and LLMR benchmarks in Table 3 and Table 4 respectively. On the SummmEdits benchmark, both LIM-A and LIM-RA consistently outperform other baselines. LIM-RA has the best overall performance and has a 5.0% relative improvement over the best baseline AlignScore. Our model achieves the best score in 8 of the 10 datasets and performs significantly better on OOD domain datasets such as Shakespeare (SP), BillSum (BILL), SciTLDR (SCI) compared to the baseline. On the LLMR benchmark, we only report AlignScore as Tables 2, 3, 5 show that AlignScore is the strongest baseline. LIM-RA achieves the best overall result and obtains a relative improvement of 6.9% over AlignScore, and has the best score on 6 of the 7 datasets.

We report the overall average score on the four benchmarks in Table 6. In summary, LIM-RA exhibits a 4.4% relative improvement over the baseline model AlignScore.

#### 3.3.3 Comparing with LLM Baselines

We compare the trained metric models with two LLMs: Mistral-7B and GPT-3.5-Turbo (ChatGPT) using the same 0-shot and 10-shot prompt (described in Appendix A.4). Since LLMs do not provide factual consistency scores, we report balanced accuracy in Table 8 and only report SummaC and SummEdits due to time constraints. LIM-RA continues to perform the best on the two benchmarks while GPT-3.5-Turbo outperforms Mistral by a large margin on SummaC. Additionally, 0-shot ChatGPT outperforms 10-shot ChatGPT on SummeDits possibly because the 10-shot demonstrations are out-of-domain. We compare average inference time of each model on a sample of data from SummaC and find AlignScore demonstrates fast inference speed of 0.18s on a single NVIDIAAG GPU followed by LIM-RA with 0.29s. The slower speed is because DeBERTa is slower than RoBERta even though they have a similar number of parameters. 0-shot ChatGPT and Mistral-7B on

\begin{table}
\begin{tabular}{l l l l l l} \hline Model & SummaC & TRUE & SummingEdits & LLMR & AVG \\ \hline AlignScore & 82.9 & 86.2 & 80.6 & 68.5 & 79.6 \\ LIM-A & 86.0 (+3.7\%) & 86.8 (+0.7\%) & 84.4 (+4.7\%) & 71.9 (+5.0\%) & 82.3 (+3.4\%) \\ LIM-RA & **87.6 (+5.7\%)** & **87.0 (+0.9\%)** & **84.6 (+5.0\%)** & **73.2 (+6.9\%)** & **83.1 (+4.4\%)** \\ \hline \end{tabular}
\end{table}
Table 6: Average AUC results and relative improvements over AlignScore on four benchmarks. The last column is the overall average of SummaC, TRUE, SummErdis, and LLMR scores.

\begin{table}
\begin{tabular}{c c c} \hline Model & Setting & Overall \\ \hline AlignScore & 4.7M & 83.2 \\ \hline RoBERTa & pre-train & 71.6 \\ DeBERTa & pre-train & 82.1 \\ \hline RoBERTa & 10\% + cleaning & 84.1 \\ DeBERTa & 10\% + cleaning & 83.6 \\ \hline RoBERTa & 10\% + cleaning + pre-train & 83.8 \\ LIM-A & 10\% + cleaning + pre-train & 86.0 \\ \hline LIM-RA & +syn robust data & 86.4 \\ \hline \end{tabular}
\end{table}
Table 7: Ablation Study 

4 GPUs using vLLM (Kwon et al., 2023) achieves comparable speed of 0.52s and 0.51s respectively while OpenAI GPT-3.5 10-shot is the slowest, primarily due the to the rate limit of a Tier-1 account3.

Footnote 3: The Tier-1 rate-limit for GPT-3.5-Turbo is 60K tokens per minute, 3.5K requests per minute, and 10K requests per day. [https://platform.openai.com/docs/guides/rate-limits/usage-tiers?context=tier-one](https://platform.openai.com/docs/guides/rate-limits/usage-tiers?context=tier-one)

### Results on Synthetic Robustness Data

In Table 9 we evaluate the models on the synthetic robustness test dataset created in section 2.3. We see LIM-A without synthetic data augmentation performs on par with AlignScore while LIM-RA performs the best and is more robust to name and number perturbations.

### Ablation Analysis

We perform ablation studies to answer the following questions: (1) What is the impact of different training data sizes? (2) What is the performance of using a pre-trained model as the alignment? (3) What is the impact of the cleaned data? and (4) What is the impact of fine-tuning RoBERTa or DeBERta as the alignment function?

To answer (1) we sweep the size from 123K (5K per dataset) to 4M (500K per dataset). From Figure 1, we see that the benchmark performance peaks at 452K and 1.6M samples for DeBERTa and RoBERTa respectively and reduces if we include more data. For (2)-(4), we report the average AUCROC score of SummaC, SummEdis, TRUE in Table 7. To answer (2), we experiment with different off-the-shelf pre-trained NLI models. The best pre-trained DeBERTa model (82.1%) outperforms the best pre-trained RoBERTa4 (71.6%) (Nie et al., 2019). To answer (3), we perform data cleaning and use 10% (452K samples) of the training data and find both RoBERTa (84.1%) and DeBERTa (83.6%) outperform AlignScore (83.2%). To answer (4), we fine-tune the pre-trained models using the cleaned data. DeBERTa (LIM-A) performance improves with fine-tuning while RoBERTa performance decreases, possibly because the pre-trained DeBERTa outperforms the pre-trained RoBERTa model. Finally, adding the synthetic robustness data can further boost the performance.

## 4 Conclusions

We propose LIM-RA, a DeBERTa based model to automatically evaluate factual consistency trained from a cleaner and smaller training set than used for AlignScore. Experimental results show LIM-RA consistently outperforms the current state-of-the-art AlignScore and other strong baselines on 4 benchmarks. In addition, the model is robust to name and number variations and is better suited for LLM outputs' factual consistency evaluation.

## References

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* A. R. Fabbri, C. Wu, W. Liu, and C. Xiong (2021)Qafacetval: Improved qa-based factual consistency evaluation for summarization. arXiv preprint arXiv:2112.08542. Cited by: SS1.
* T. Falke, L. F. Ribeiro, P. Ajje Utama, I. Dagan, and H. Gurevych (2019)Ranking generated summaries by correctness: an interesting but challenging application for natural language inference. In Proceedings of the 57th annual meeting of the association for computational linguistics, pp. 2214-2220. Cited by: SS1.
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* X. Fu, M. R. Laskar, C. Chen, and S. Bhushan TN (2023)Are large

\begin{table}
\begin{tabular}{c c c c c} \hline Model & SC & SE & Time & GPUs \\ \hline \multicolumn{5}{l}{Mistral-JB 10-shot} & 62.0 & 64.0 & 0.51s & 4 \\ GPT-3.5-shot & 73.5 & 71.6 & 0.52s & API \\ GPT-3.5-10-shot & 76.7 & 69.8 & 8.4s & API \\ AlignScore & 74.0 & 71.9 & 0.18s & 1 \\ LIM-A & 77.8 & 76.5 & 0.29s & 1 \\ LIM-RA & **78.5** & **76.7** & 0.29s & 1 \\ \hline \end{tabular}
\end{table}
Table 8: Evaluation using LLMs Balanced Accuracy results and Average Inference Time on SummaC (SC) and SummEdits (SE).

\begin{table}
\begin{tabular}{c c c} \hline  & Robust-Name & Robust-Number \\ \hline Train (Test) & 19,508 (3.492) & 20,628 (5,076) \\ \hline AlignScore & 64.3 & 86.4 \\ LIM-A & 64.0 & 88.0 \\ LIM-RA & **84.8** & **91.8** \\ \hline \end{tabular}
\end{table}
Table 9: Synthetic robustness data size and AUC-ROC performance across models when facing perturbed data.

 

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## Appendix A Appendix


Given a name, modify one or two letters to change it to ad different Original Text: 154 name. Rephrase Text: one hundred fifty-four Original Text: Abraham Lincoln Changed Text: Abraham Lincoln Original Text: morethan 10 years ago Original Text: cricket Rephrase Text: more than ten Changed Text: cracket yearsago Original Text: Wireshark Original Text: second Changed Text: Wileshark Rephrase Text: 2nd Original Text: Robert Urquhart. Original Text: 22 June 1990 Changed Text: Robert Uruhart. Rephrase Text: June twenty-two nineteenth entirety Original Text: Dees Smith Changed Text: Dees Smyth Original Text: at leastone Rephrase Text: at lesti 1 Original Text: 25 Years Changed Text: 26 November 2018

#### A.3.2 Prompt to perturb numbers

Change the meaning of the text. Original Text: 37 Changed Text: 27 Original Text: morethan 10 years ago Changed Text: morethan 11 years ago

Original Text: morethan 11 years ago Changed Text: morethan 11 years ago

Original Text: morethan 10 years ago Changed Text: within 10years

Original Text: second Changed Text: third Original Text: 22 June 1990 Changed Text: 22 July 1990

Original Text: at leastone Changed Text: atmostone Original Text: 2 years Changed Text:

#### A.3.3 Prompt to rephrase numbers

Rephrase thenumbers in thetext.

 

Answer: 2

Context: Thebesheldontopower

untilthe12th Dynasty, when

itsfirstking, AmenemhetI who

reignedbetween19801951b.c.

establishedacapital

near Memphis.

Claim: ThecapitalnearMemphis