PivotFEC: Enhancing Few-shot Factual Error Correction with a Pivot Task Approach using Large Language Models
Authors: Xingwei He
A-Long Jin
Jun Ma
Yuan Yuan
Siu Ming Yiu
Venue: EMNLP 2023 Findings (2023)
Keywords: Factual Error Correction
Large Language Models
Few-shot

Abstract: Factual Error Correction (FEC) aims to rectify false claims by making minimal revisions to align them more accurately with supporting evidence. However, the lack of datasets containing false claims and their corresponding corrections has impeded progress in this field. Existing distantly supervised models typically employ the mask-then-correct paradigm, where a masker identifies problematic spans in false claims, followed by a corrector to predict the masked portions. Unfortunately, accurately identifying errors in claims is challenging, leading to issues like over-erasure and incorrect masking. To overcome these challenges, we present PivotFEC, a method that enhances few-shot FEC with a pivot task approach using large language models (LLMs). Specifically, we introduce a pivot task called factual error injection, which leverages LLMs (e.g., ChatGPT) to intentionally generate text containing factual errors under few-shot settings; then, the generated text with factual errors can be used to train the FEC corrector. Our experiments on a public dataset demonstrate the effectiveness of PivotFEC in two significant ways: Firstly, it improves the widely-adopted SARI metrics by 11.3 compared to the best-performing distantly supervised methods. Secondly, it outperforms its few-shot counterpart (i.e., LLMs are directly used to solve FEC) by 7.9 points in SARI, validating the efficacy of our proposed pivot task.

PivotFEC:
Enhancing Few-shot Factual Error Correction with a Pivot Task Approach using Large Language Models
Xingwei He', A-Long Jin', Jun Ma', Yuan 23.41, Siu Ming Yiu
'The University of Hong Kong, Hong Kong, China
School of Computer Science and Engineering, Beihang University, Beijing, China State Key Laboratory of Software, Development Environment, "Zhongguancun Laboratory hexingwei 50gmai 1. com, ajinêeee. hku. hk, junmačhku. hk, yuan2 1amnaa.buaa... edu. cn, smyiuecs. hku. hk
Abstract
has amassed over 100 million users. However, one drawback of ChatGPT is its tendency to gen- crate text that is nonsensical, or unfaithful to the provided source input, referred to as hallucination (Maynez et al., 2020; Raunak et al., 2021). To address this issue, the research community has ded- icated efforts to the development of factual error correction (FEC), aiming rectify false claims with minimal modifications to make them better supported by the given evidence. Consequently, research focused on this task plays a crucial role mitigating the problem of hallucinations in LLMs.
Factual Error Correction (FEC) aims to rec- tify false claims by making minimal revisions to align them more accurately with support- ing evidence. However, the lack of datasets containing false claims and their correspond- ing corrections has impeded progress this field. Existing distantly supervised models typ- ically employ the mask-then-corett paradigm, where a masker identifies problematic spans in false claims, followed by corrector to predict the masked portions. Unfortunately, accurately identifying errors claims is challenging, lead- ing issues like over-erasure and incorrect masking. To overcome these challenges, we present PivotFEC, method that enhances few- shot FEC with pivot task approach using large language models (LLMs). Specifically, we in- troduce a pivot task called factual error injec- tion, which leverages LLMs (e.g.. ChatGPT) to intentionally generate text containing factual errors under few-shot settings; then, the gen- erated text with factual errors can be used to train the FEC corrector. Our experiments on a public dataset demonstrate the effectiveness of PivotFEC in two significant ways: Firstly, it improves the widely-adopte SARI metrics by 11.3 compared to the best-performing distantly supervised methods. Secondly, it outperforms its few-shot counterpart (i.c., LLMs are directly used to solve FEC) by 7.9 points in SARI, vali- dating the efficacy our proposed pivot task.
The most straightforward way to develop FEC systems is by fine-tuning pre- pre-trained models, such as BART (Lewis et al., 2020a) and T5 (Raffel et al.. 2020), on parallel data, consisting of false claims along with their corresponding corrections. Nev- ertheless, the availability of such paired data is restricted due to the tremendous labor and time required for human annotations.
To overcome the data scarcity, researchers (Shah et al., 2020; Thorne and Vlachos, 2021; Chen al., 2023) make use of distant supervision from the fact verification dataset, FEVER (Thorne et al., 2018). 8). FEVER is large resource consisting of claims paired with evidence from Wikipedia, where each instance is labeled either SUPPORTED or RE- FUTED based whether the claim is supported or refuted by the corresponding evidence. Exist- ing distantly supervised models typically follow the mask-then-correct approach (Shah et al., 2020; Thorne and Vlachos, 2021). Concretely, the fact verification classifier (FVC), trained on FEVER, acts as the masker, designed to find problematic spans within false claims. The token-level expla- nations (Ribeiro et al., 2016; Chen al., 2017) of FVC are usually exploited as masks. The corrector is trained on the SUPPORTED data from FEVER. with the objective inal/correct claim based on the masked claim and evidence, during training/inference. Furthermore,
1 Introduction
ChatGPT an artificial intelligence chatbot re- leased by OpenAl on November 30, 2022 and built upon on the company's Generative Pre- trained Transformer (GPT) series of large language (LLMs) (Ouyang et al., 2022; OpenAl, 2023). Since its launch, ChatGPT has garnered signifi- cant global attention due to its comprehensive and eloquent responses across various knowledge do- mains. Within just two months, by January 2023, :Corresponding authors.
restoring/gennrating the orig-

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contrast, the FEI task allows for multiple ways to introduce factual errors into a correct claim. For example, one can replace "New York" with "Los Angeles", substitute "2013" 3" with "2012", or even insert negative word such "nor" into the correct claim. This distinction demonstrates that the FÉl task encompasses con- siderably larger solution space compared to FEC. By exploring this expanded solution space, we can leverage the relatively easier nature of FEI to en- hance the overall performance FEC systems. Correct Claim
Wrong Claim
Evidence
Correct:
The 2013 NBA draft was held in Pennsylvania.
The 2013 NBA draft was held in New York
Page: NBA
Context: The 2013 NBA draft held June 27, 2013, at Barclays Center Brooklyn, New York.
Retrieve
Wrong Claim
The 2013 NBA draft held Los Angeles.
Claim
The 2012 NBA draft was held New York.
Destroy:
The 2013 NBA draft held in New York.
The 2013 NBA draft was held in New York
Figure I: The comparison between factual error correction and factual error injection.
Chen et al. (2023) propose using the mask-then- correct method to iteratively refine the false claims instead of relying
with LLMs in few-shot settings. (2) Compared with distantly supervised baselines, PivotFEC only re- quires 8 labeled samples from FECDATA, eliminat- ing the need for labeled data, FEVER, to train the FVC. (3) Extensive experiments conducted the FECDATA datuset demonstrate that PivotFEC out- performs distantly supervised baselines by large margin, achieving new state-of-the-an (SOTA) re- sult on the test set with scores 66.3 on SARI and 66.68 on ROUGE-2. (4) PivotFEC exhibits much better performance than its few-shot counterpart (66.3 vs. 58.43 on SARI), where LLMs are directly used to solve FEC, confirming the effectiveness and necessity of our proposed pivot task.
single pass, However, accu- rately identifying factual errors using FVC is non- trivial. This limitation often leads over-erasure and incorrect masking issues, becoming bottle- neck that restricts the performance of FEC models.
To bypass these issues, we propose to solve the FEC task by introducing a pivot factual error injection (FEI), aiming to generate false claims by injecting factual errors into correct claims. Our main motivation is that the FEI task is relatively easier than the FEC task. As shown in Figure 1, FEC model is expected to precisely identify the factual error in the false claim "The 2013 NBA draft was held in Pennsylvania." " and correct it to "The 2013 NBA draft was held in New York." based on the given evidence.
Problem Statement
Factual error correction aims revise the factual errors claim C with minimal edits and generate a revised claim C" based on the provided evidence E. C" should be grammatical, supported by the evidence, and correct the factual errors in C.
3 Preliminary
In this section, we will introduce in-context learn- ing and how to solve FEC using LLMs with in- context few-shot learning via prompting.
Our second motivation stems from the fact that LLMs, such GPT-3.5, can serve as an excellent data annotator in few-shot settings, rivaling even surpassing the performance of crowdsourced anno- tators (He et al., 2023). Inspired by this, we use LLMs solve the FEI task, specifically generating false claims for correct claims. By doing so, we obtain a sufficient amount of paired data, which will be further used to train the FEC corrector.
3.1 LLMs with In-context Learning
LLMs, especially ChatGPT, have demonstrated remarkable few-shot capability various down- stream tasks. Therefore, it is natural to employ LLMs for addressing the FEC task in few-shot setting. Building upon the approach introduced by GPT-3 (Brown et al., 2020), utilize LLMs with in-context few-shot learning through prompting tackle FEC. Rather than fine-tuning LLMs specifi- cally for individual tasks, we can efficiently prompt the model by providing a small set of input-output exemplars that demonstrate the task.
Our contributions are summarized as follows: (I) We propose PivotFEC! method that uses Pivot task, factual error injection, to enhance FEC
OOur code is available at: NLPCode/PivotFEC.

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Factual Eroo Injaction
Synthetic
Training Corrector
The Invention of Lyyngg Tha supporting cast foatunes Jennifer Garner, Jonah Hil, Lou's C.K. Rob Lawe and Tina Fey. Original claim: The Invention of Lyynn's s cast includes Louis C.K.
Evidence:
Original claim:
The
Mutated claim: The Invention of Lying's 's cast excludes Louis C.K.
draft was held in New York. Mutated claim: The 2013 NBA held Los Angeles.
Encoder
Decoder
Evidence: 2013 NBA draft: The 2013 NBA draft was held on June 27, 2013, Berclays Center in Brocklyn, New York, Original claim:: 2013 draft
Mutated Clamm
Evidence
Original Claim
New Mutated claim: The 2013 NBA draft was held in Los Angeles.
Figure 2: The PivotFEC method contains three steps: (1) prompt LLM using FEI prompting (Red text indicates ChatGPT's output, while the rest is the input); (2) collect the generated data as the synthetic FEC data; (3) train the factual error corrector with the synthetic data. For simplicity, the process evidence retrieval has been omitted.
3.2 Factual Error Correction with LLMs
Factual Error Correction
To address FEC using LLMs, we begin by choos- ing a set of demonstrated examples. Each example comprises three elements: a gold evidence, a mu- tated claim, and an original claim. The objective is for LLMs to learn how to modify the mutated claim based on the provided samples. Figure 3 illustrates simplified prompt where the LLM (in this case. ChatGPT) accurately corrects the factual error in the mutated claim by replacing "Los Angeles" with "New York." For the full prompt of the FEC task, please refer to Table in Appendix C.
Evidence: The Invention of Lying: The supporting cast features Jennifer Garner, Jonah Hill, Louis C.K., Rob Lowe and Tina Fey. Mutated claim: The Invention Lying's excludes Louis C.K.
Original claim: The Invention of Lying's 's cast includes Louis C.K.
Evidence: 2013 NBA draft; The 2013 NEA draft held June 27, 2013, at Barclays Center in Brookdyn, New York.
held Angeles. Original claim: The 2013 NBA draft was held in New York.
claim: The
Factual error correction with in-context learn- ing using ChatGPT, Text in red color denotes the output of ChatGPT, while the remaining parts are the input.
Figure 3:
sequently, previous approaches suffer from issues such as over-erasure and incorrect masking.
4 Approach
In this section, we will first provide overview of PivotFEC $4.1. As illustrated in Figure 2, our method comprises three main steps. We will begin by introducing the FEI task and demonstrating the utilization of LLMs to address the FEI task in $4.2. Next, we will present the process of gathering syn- thetic paired data for the FEC task in $4.3. Finally, we will demonstrate the training of the corrector using the synthetic data in $4.4.
Our primary motivation claims by injecting factual errors into correct claims. This allows us to obtain FEC data con- sisting of correct claims paired with their corre- sponding false claims, which can be directly used for training the FEC corrector. To achieve this goal. introduce the pivot task, factual error injection (FEI,, for FEC, and then employ LLMs to address the FEI task using a few-shot in-context learning approach. Compared to the previous mask-then- correct method, our approach climinates the need to mask factual errors before correction, thus avoid- ing the over-erasure and incorrect masking issues. Moreover, our approach does not depend on labeled fact verification data. Instead, only require cor- rect claims and few labeled FEC samples.
generate false
4.1 Overview
The main limitation in developing FEC systems is the scarcity of paired data comprising correct claims and their corresponding false claims. To mit- igate this limitation, previous studies (Shah et al., 2020; Thome and Vlachos, 2021; Chen et al., 2023) follow the mask-then-correct method, with the as- sumption that there are sufficient human annotated fact verification data (i.e., FEVER), which is used to train the FVC. They train the corrector by mask- ing certain portions of correct claims and then re- covering them. Therefore, during testing, be- comes necessary to identify factual errors within false claims and mask these errors with the FVC before using the corrector to revise them. Con-
4.2 Factual Error Injection with LLMs
To address FEC, LLMs are expected to identify factual errors and correct them. As previously analyzed, FEI requires LLMs to introduce fac- tual errors into correct claims and has a signifi- cantly larger solution space than FEC (see Figure 1). Therefore, we assume that FEI is comparatively easier for LLMs than FEC. This is why we intro-

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duce this pivot task for FEC.
Label
Train
Valid
Test
Similar to FEC, we also employ LLMs tackle FEI using the few-shot in-context learning ap- proach. For fair comparisons, we utilize the same demonstrated exemplars as used in few-shot FEC. with the only difference being the order of the orig- inal claim and mutated claim. The left portion of Figure 2 illustrates simplified prompt for FEI, where the LLM (specifically ChatGPT) injects factual error by substituting" York" with "Los Angeles." For the complete prompt of the FEl task, please refer to Table Appendix D.
SUPPORTED
37,961
1.477
1,593
REFUTED
20.075
2,091
2,289
Total
58,036
3,568
3,882
Table I:
The basic statistics of FECDATA with the number of data instances for each split and label.
FEVER dataset involves two main steps: claim gen- eration and claim labeling, In the claim generation phase, annotators extract the original claims (i.e., correct claims) from Wikipedia, and then use six types of mutation: paraphrasing, negation, substi- tution an entity/relation with a similar/dissimilar one, and making the claim more gencral/specific to generate mutated claims for original claims. In the claim labeling phase, annotators classify claims SUPPORTED, REFUTED or NOTENOUGHINFO based whether the claim is supported, refuted unverifiable by the given evidence.
4.3 Creating Synthetic Data for FEC
We assume that correct claims are readily avail- able. For each correct claim C inject factual errors into Ot via in-context learn- ing with the prompt in Table 7. The generated claim is referred as C By doing so, we collect the synthetic data for FEC, where Cj and C/ denote the i-th correct claim and the corresponding false (i.e., generated) claim, respectively.
LLMs to
[(C), C (C;, C
FECDATA extracts the SUPPORTED and RE- FUTED data instances from FEVER, and uses the original claims and mutated claims as the paired data. Table shows the basic statistics of this dataset. To gain further insights, we provide Fig- ure in Appendix A, displaying the distribution of mutation types for the REFUTED claims.
4.4 Training FEC Corrector
After obtaining the synthetic data D, we acquire the FEC corrector by fine-tuning pre-trained lan- guage models, such as BART or T5 on this data. To be concrete, we concatenate the false claim Cf and the gold evidence retrieved evidence, and di- rectly input them into the encoder (refer the right part of Figure 2 for the input format). For more detailed information on obtaining evidence for the false claim, please refer to Appendix B.I. During training, we optimize the corrector by maximizing the conditional probability of C
Evaluation Metries. For automatic evaluation. we resort SART (Xu al., 2016)² and ROUGE-2 (Lin, 2004)² metrics. The SARI metric explicitly assesses the goodness of words in the revised claim that are added, deleted and kept by FEC models from the source (mutated claim), compared with the referenced ground truth (original claim). We report the n-gram Fl score for "keep" operations (Keep), the n-gram precision score for "delete" op- crations (Delete), the n-gram Fl score for "add" operations (Add), and the average of these three scores (Final). ROUGE-2 measures the surface- level similarities between revised claims and refer- ence claims. The SARI Final score serves as the primary evaluation metric due to its strong positive correlation with manual evaluation, as indicated by Thome and Vlachos (2021)'s statistical findings.
N P(C 0) II plCnnCiiee C/ E;;0) x=1
where 0 represents the parameters of the corrector, E; denotes the corresponding evidence, and Orjicn refers to the sub-sequence preceding C
5 Experiment
Baselines. We consider three types of baselines: Fully Supervised Baselines estimate the ceiling performance of FEC models, under the assump-
5.1 Experimental Setups
Dataset. Following previous work, we evalu- ate our model on the evidence-based FEC dataset (FECDATA) (Thorne and Vlachos, 2021), created based on the large fact verification dataset, FEVER (Thome et al., 2018). The construction the
"The evaluation code for SARI available at: https: seeegginnace.coom sesacee/evallaatemeteeee
'The evaluation code for ROUGE available at: https:
//hugging' ingf ace.. wwwaaac....cccceeeeeeeeeeeeeeeee

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tion that substantial amount of data is accessible. For this purpose, we fine-tune BART-base and T5- base on FECDATA, where the encoder takes the false claim and corresponding evidence as inputs, while the decoder generates the revised claim.
create false claims, In this approach, named enti- ties within the correct claims are substituted with [MASK] tokens. The masked claims are then pro- cessed through the BART-base model to generate new claims by filling in the [MASK] tokens. These newly generated claims are considered as false claims. This method is termed MaskEntity.
Distantly Supervised Baselines adopt the mask-thee--crreed pipeline, consisting of masker and a corrector. The masker can take var- ious forms, such as the token-level explanations (Ribeiro et al., 2016; Chen et al., 2017) of a fact verification classifier (FVC), random masking, or heuristic masking. The FVC is initialized with BERT-base (Devlin et al., 2019) or RoBERTa-large (Liu et al., 2019), and trained on FEVER. On the other hand, the corrector is trained exclusively on the SUPPORTED data instances from FEVER. (1) Dual encoder pointer network (DEPN) (Shah et al., 2020) utilizes an FVC to predict masked words and subsequently generates a revised claim using the dual encoder pointer generator with the copy mech- anism (See et al., 2). (2) T5 Masker- Corrector (T5MC) (Thorne and Vlachos, 2021) differs from DEPN in two main ways: (a) The corrector (i.e., generator) is based on T5-base. (b) It randomly masks words in the imput claim during training, but during inference, heuristic masking employed, where words do not present in the evidence are masked. (3) T5MC-MLM differs from T5MC in that it uses the masked language model BERT as the masker during inference. (4) T5MC-V is vari- ant of TSMC, using FVC as the masker to predict the masked tokens. (5) VENCE (Chen et al., 2023) iteratively executes steps of mask-then-ccoree over the claim to make supported by the evidence. base- Baselines first generate synthetic paired data, and then train factual error correctors on them. Rule-based methods are employed to con- struct the inconsistent summaries with the aim of improving the faithfulness in abstractive summa- rization (Cao et al., 2020; Cao and Wang, 2021). We begin by utilizing spaCy", a free open-source library for NLP, to recognize name entities in cor- rect claims, and then implement two rule-based baselines for factual error correction: (1) The first rule-based method creates false claims by swap- ping named entities from the correct claims with alternative entities of the same entity type randomly chosen from the training dataset. This method re- ferred to as SwapEntity. (2) The second based baseline resorts to the 'mask-then-fill pipeline to
Few-shot Baselines contains two models: 8- shot TS-base fine-tunes T5-base using 8 data ex- amples. 8-shot ChatGPT revises false claims by prompting ChatGPT with 8 demonstrated exam- ples. For fair comparisons, the few-shot baselines and our PivotFEC use the same set of examples.
Implementation Details. Our implementation details are shown in Appendix B.
5.2 Experimental Results
The main experimental results on the FECDATA test set in Table 2 reveal the following key findings: LLMs exhibit remarkable few-shot ability for FEC. Directly fine-tuning T5 the 8 labeled data instances (i.e., 8-shot T5-base) does not bring any improvement over previous distantly supervised baselines, such as VENCE. However, the few-shot in-context learning baseline (i.e., 8-shot ChatGPT) achieves a noteworthy SARI Final score of 58.43, surpassing VENCE (RoBERTa) by approximately 3 points. These results highlight the impressive few-shot capability of ChatGPT.
Our proposed pivot task is highly effective for few-shot FEC. To demonstrate the effectiveness of our proposed PivotFEC, we compare with the 8-shot ChatGPT model. To ensure fair compar- isons, both few-shot models are based on ChatGPT. As shown in Table 2, our proposed model, 8-shot PivotFEC far exceeds its few-shot counterpart (8- shot ChatGPT) by a significant margin across all metrics. The SARI metric increased from 58.43 to 66.30 and the ROUGE-2 score increased from 49.43 to 66.68.
On the other hand, PivotI'EC also notably out- performs the rule-based baselines. Additionally, PivotFEC achieves the peak performance with just 2,000 synthetic data instances for training the cor- rector, while synthetic data instances to reach their peak perfor- mance when training the correctors. These results can be attributed the enhanced quality of false claims produced by ChatGPT. Rule-based methods, by contrast, often produce false claims with subop- timal quality in two key aspects: (1) Grammatical
methods require 10,000
io/

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Retrieved Evidence
Gold Evidence
Models
FVC
SARI Score
Score
RG-2
RG-2
Keep
Delete
Add
Final
Keep
Delete
Add
Final
Fully Supervised Baselines
Supervised BART-basc" Supervised TS-base"
70.75
65.65
38.88
58.43
59 99
73.51
69.27
47.30
63.36
64.00
85.40
88.92
48.40
40
74.24
73.50
88.56
91.40
58.38
79.45
78.04
Distantly Supervised Baselines
DEPN (Shah et al., 2020)
34.5
48.1
1.7
28.1
34.8
45.2
56.9
3.9
35.3
TSMC (Thorne and Vlachos, 2021)!
65.2
62.7
15.5
47.8
50.3
66.7
62.2
16.1
48.3
Fnumerate
Bb
66.2
64.3
17.1
49.2
51.2
T5MC-MLM
52.9
38.9
42.7
TSMC-V (Thore and Vlachos, 2021)*
Bb
61.1
54.3
19.4
44,9
42.0
61,8
62.2
10.2
44.7
Enumerate
Bb
63.0
55.7
24.1
47.6
45.5
60.1
34.8
53.6
57.7
67.5
61.5
54.5
VENCE (Chen al., 2023)*
36.0
55.0
Rule-based Baselines
SwapEntity"
67.95
94.57
16.62
59.71
62.06
70.32
97.72
22.25
63.43
64.88
MaskEntity"
70.31
94.37
19.04
61.24
63.49
73.91
94.92
27.81
65.55
66.45
Few-shot Baselines
8-shot T5-base"
61.75
85.23
8.70
51.89
49.83
63.50
82.44
13.56
53.17
51.38
8-shot ChatGPT
72.09
75.92
27.29
58.43
49.43
79.98
81.61
38.81
66.80
60.72
Few-shot (Our Method)
8-shot PivotFFC (ChatGPT))
76.51
92.61
29.78
66.30
66.68
79.62
93.82
39.21
70.89
70.41
Table 2: Automatic evaluation results (%) of model and baselines with retrieved evidence ground truth evidence on the FECDATA test set. Results marked with 1. f, and * are from VENCE (Chen et al., 2023), TSMC-V (Thorne and Vlachos, 2021) and our reproduction, respectively. Bb and RI denote BERT-base and RoBERTa-large. Enumerate refers to using the FVC model to rank 20 generated claims and select the best one. underline indicates the best model and bold indicates the second best. RG-2 refers to ROUGE-2.
errors might be present in generated false claims. (2) False claims generated by rule-based methods might deviate from the original topics present in correct claims. With ChatGPT's remarkable in- context learning capabilities, injecting factual er- rors into correct claims hardly introduces grammat- ical errors or deviates from the original topics.
improves the SARI Final score by approximately 4 points. This demonstrates the inadequacy of re- trieved evidence, which aligns with previous find- ings (Thorne and Vlachos, 2021). As our work mainly focuses on improving FEC through the in- troduction the pivot task, we defer the improve- ment evidence retrieval to future work.
These compelling improvements establish new SOTA result and provide strong evidence for the effectiveness of the pivot task in enhancing the per- formance of FEC.
5.3 More Analysis and Discussion
The experiments conducted in this section utilize ChatGPT with 8-shot in-context learning and re- trieved evidence, if there is no particular statement.
PivotFEC lags behind supervised baselines. While PivotFEC outperforms distantly supervised and few-shot models, there still exists a significant performance gap compared to supervised methods. For example, the supervised T5-base achieves a score of 74.24 on SARI Final, whereas PivotFEC only scores 66.30, indicating that there is ample room for further improvement.
Effect of the Number of Synthetic Data. To show the effect of synthetic data generated with FEI on PivotFEC, we first generate synthetic data for FEC with 8-shot PivotFEC (ChatGPT), and then fine-tune T5-base on varied numbers of synthetic data instances. For comparison, we also evaluate the performance of T5-base trained data (FECDATA)) As shown in Figure 4 (a), when the data size does not exceed 1k, the performance of 8-shot PivotFEC increases linearly with the in- crease of data, even matching the performance T5-base trained on the gold data. Nevertheless, our
the gold
The retrieved evidence is inadequate compared with gold evidence. To further explore the ceiling performance of our method, we conduct experi- ments using gold evidence as well. The results reveal that when using gold evidence, PivotFEC

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75
85.54
64/64
65:78
64
66.24
66
SAR Real (8))
70
60.55
65
57.53
$8.43
58:17
55,68
Supervised TS-base
Few-shot ChatGPT
B-4hot
8-shot PlvoFFEC (ChatGPT)
Few-shot PlvorFEC (ChatGPT)
-
B-shot PivoFFEC
55 50 100.00 1k 6k Lök 58036
test ten betboge
sta-oune
gped S-urbe
(a) Numher of training data instances
(b) k-shot in-context learning
(c) LLMS with 8-shot in-context learning
Figure 4: Subfigure (a) shows the performance on the test set for T5-base trained with different numbers of gold or generated data instances. Subfigure (b) shows the performance on the test set for different few-shot in-context learning. Subfigure (c) shows the performance the set for different L.LMs with 8-shot in-context learning.
model's performance plateaus once the data size reaches 2k. In contrast, T5-base trained on gold data continues to improve. Even when using all FECDATA training data, its performance does not reach its peak. This observation suggests that the generated data contains noise compared to the gold data, limiting the upper performance of PivotFEC.
SARI Score
Mutation Types
RG-2
Keep
Delete Add
Final
Negate
76.51 92.61 29.78 66.30
66.68
Substitute similar
73.72
92.08
25.16
63.66
64.97
Substitute dissimilar Specific
69.80
92.47
17.99
60.09
61.43
71.33
92.52
22.53 62.13 64.09
Table 3: Evaluation results (%) of PivotFEC with prompts created using examples from different mutation types on the test set. RG-2 denotes ROUGE-2.
Effeet of the Number of In-Context Examples. Table 2 demonstrates the superiority of PivotFEC over the few-shot FEC with ChatGPT under the 8-shot setting. To further validate the effectiveness of PivotFEC, we compare its performance with the few-shot FEC using ChatGPT with varying num- bers of in-context examples. As depicted in Figure 4 (b), PivotFEC exhibits a notable improvement of approximately 7 to 10 points on the SARI Fi- nal score compared to the few-shot FEC model at different shots. When utilizing demonstrated examples, model reaches a plateau.
Negate
Substitute dissimilar
Substitute
Specific
80
SARI
70-
IILAM Sub_sim dissim Specific
60
Gold
Negate
Figure 5: Results on different test cases, with the test set being divided according to mutation types. Gold notes T5-base trained on FECDATA. Negate, Sub sim, Sub dissim, and Specific refer to PivotFEC with their respective mutation prompts.
Effect of Different LLMs. To further empha- size the advantages of our method, we compare it with 8-shot FEC across different LLMss, includ- ing three InstructGPT models (text-ada-001, text- babbage-001, and text-curie-001) and ChatGPT (gp--3---turb-0000)) Figure 4 (c) illustrates our method consistently outperforms the few-shot FEC baseline across different LLMs. Moreover, both our method and the baseline exhibit noticeable per- formance improvements as the model parameters increase. However, even with small models, our method still performs exceptionally well. For exam- plc, the smallest model falls only around points behind the largest model. In contrast, the baseline's performance heavily influenced by the choice of model, particularly evident in text-ada-001, which experiences a decrease of approximately 12 points compared to the larger model, text-curie-001.
Effect of Mutation Types. As shown in Figure 6 in Appendix A, the refuted claims mainly stem from four mutation types. Therefore, construct four prompts, each composed of examples from the corresponding mutation. For prompts consisting of examples from negate, substitute similar, substitute dissimilar, and specific mutations, please refer to Tables 7, 8, 9, and 10. Table 3 shows that PivotFEC with negate prompt yields the best results, possibly because this mutation constitutes the largest por- tion of the test set. Additionally, we present the performance of PivotFEC on separate test cases. Figure illustrates that: (1) PivotFEC performs well on test case of the specific mutation type when using prompt tailored to that type, and (2) the variations in Piv performance, when us-

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Models
Grammar Support Correct
make the minimal edits. As for the second exam- plc, 8-shot ChatGPT fails to identify the error in the false claim, resulting in a text that exhibits a low correlation with the original claim. This also explains why this method achieves a relatively high supported value but demonstrates a low corrected score during the human evaluation. Most notably, our method can accurately identify errors and make modifications based on the retrieved evidence, sim- ilar to the performance of the supervised T5 model.
Fully supervised models with gold evidence
T5-base
100
89.3
86.7
8-shot models with retrieved evidence
T5-base
83.3
22.0
5.3
CharGPT
92.0
90.0
42.0
PivotFEC (ChatGPT) 99.3
65.3
54.7
Table 4: Human evaluation results (%) on the test set for the grammatical (Grammar), supported (Support) and corrected (Correct) scores.
ing different prompts, mainly arise from the negate case.
Related Work
6.1 Grammatical Error Correction
5.4 Human Evaluation
Grammatical error correction (GEC) (Ng et al., 2014; Yuan and Briscoe, 2016; Bryant et al., 2017: Awasthi et al., 2019; Liu et al., 2021) refers the process of identifying and rectifying grammat- ical errors in written text. It has practical appli- cations several domains, such as helping non- native speakers enhance their writing skills, aiding language learners in improving their grammatical accuracy, and assisting professional writers in pro- ducing error-free and polished content. GEC aims to improve the accuracy and fluency of the lan- guage by fixing various grammatical errors, includ- ing missing prepositions, mismatched subject-verb agreement, misspellings, and word choice errors. In comparison, factual error correction involves cor- recting the factual errors instead of the grammatical errors in the given content, such as incorrect dates, names, or historical events.
We conduct a human evaluation to compare Piv- ofFEC with the fully supervised T5-base, 8-shot T5-base and 8-shot ChatGPT models. The fully supervised ba-base model utilizes gold evidence to rectify false claims, while the others use retrieved evidence. For each model, we randomly sample 50 cases and ask three annotators to assess the revised claims based on the following Boolean questions: (I) Is the revised claim grammatically correct? (2) Is the revised claim supported by evidence? (3) Has the factual error in the false claim been corrected? The final question, measuring the correction of fac- tual errors, is the most important metric in our human evaluation. As shown in Table 4, our pro- posed model outperforms the few-shot baselines on the corrected metrie; however, there is still a gap to reach the ceiling performance of the supervised baseline. Inter-annotator agreement measured by Fleiss' kappa (Fleiss, 1971) is 0.75, 0.86, and 0.81 for grammatical, supported, and corrected scores, implying substantial agreement (> 0.6) (Landis and Koch, 1977).
6.2 Retrieval-Augmentee Generation
Retrieval-angmented generation (Lewis et al., 2020b) combines the power of information re- trieval and language generation techniques el- evate the overall quality of the generated content. For example, (He et al., 2022) use dense retriev- ers to retrieve relevant sentences from an exter- nal corpus for the given keywords to improve lexically constrained text generation (He and Li, 2021; He, 2021). By incorporating external knowl- edge, retrieval- augmented generation effectively mitigates the risk of generating inaccurate sensical content. Furthermore, factual error correc- tion another facet of aaugmeella genera- tion. It rectifies factual inaccuracies based on the evidence retrieved, thereby fitting under the broader umbrella of retrieval -uugmeente generation.
5.5 Samples and Analysis
Table 5 presents the revised claims generated by our approach and the baselines. From this table, we observe that the 8-shot T5-base mothod cannot identify errors in false claims. Similarly, 8-shot ChatGPT often struggles to precisely locate errors within false claims, and tends to simply copy con- tent from the evidence into the modified claims rather than correct them. For example, in the first example, although 8-shot ChatGPT corrects the factual error in the original sentence, it does not
All annotators hold Ph.D. degrees and are independent from our research.

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Gold Claim: The SpongeBob SquarePants Movie is film.
False Claim: The SpongeBob SquarePants Movie dog.
Retrieved Evidence: title: The SpongeBob SquarePants Movie; context: The SpongeBob SquarePants Movie a 2004
American animated adventure comedy film based on Nickelodeon television series SpongeBob SquarePants...
Supervised T5-base: The SpongeBob SquarePants Movic is film.
8-shot TS-base: The SpongeBob SquarePants Movie is a dog.
X
8-shot ChatGPT: The SpongeBob SquarePants Movie is un animated film based on the Nickelodeon television scries SpongeBob SquarePants. V
8-shot PivotFEC (ChatGPT): The SpongeBob SquarePants Movie is film.
Gold Claim: The Hunger Games are based on a novel.
False Claim: The Hunger Games are based on play.
Retrieved Evidence: tide: The Hunger Games; context: ... The Hunger Games is a trilogy of young adult dystopian novels written by American novelist Suzanne Collins...
Supervised bS-base: The Hunger Games are based on novel.
8-shot T5-base: The Hunger Games are based on play. X
8-shot ChatGPT: The Hunger Games is trilogy of young adult dystopian novels.
X
8-shot PivolFEC (ChatGPT): The Hunger Games are based on novel.
Table 5: Revised claims generated by our model and baselines based on the evidence for false claims extracted from the test set. The supervised T5-base revises false claims based on the gold evidence, while others utilize retrieved evidence. For simplicity, we do not show the gold evidence. Text in blue, red, and orange colors represents factual errors, correct modifications, and copied text, respectively.
6.3 Fact Verification
previous distantly supervised baselines, establish- ing new SOTA performance FECDATA. Fur- thermore, our approach significantly outperforms its few-shot counterpart, providing strong evidence for the effectiveness of the pivot task.
Fact verification, also known fact-checking, aims validate the accuracy of a given claim by examining the available evidence. This field of study has been extensively researched in recent years. Researchers assess the claim by analyz- ing both unstructured sources, such as political news (PolitiFact) (Vlachos and Riedel, 2014; Wang, 2017), Wikipedia (FEVER) (Thorne et al., 2018; Liu et al., 2020), and scientific literature (Wadden et al., 2020), as well as structured sources, includ- ing Wikipedia tables (TabFact) (Chen et al., 2020) and knowledge base (Iso et al., 2020). Fact verifi- cation seeks to determine whether a claim is sup- ported or refuted by evidence, while factual error correction takes it step further. Factual error cor- rection not only involves identifying factual errors but also requires modifying them to obtain correct claims.
Limitations
There are two potential limitations to this study. Firstly, due to limited computational resources, only assess the effectiveness of our proposed method on GPT series models. Future work should include additional experiments with other language models, such as PaLM and LLaMA. The second limitation is that the retrieved evidence may not al- ways be relevant to the input claim, which means it may not provide useful information for correcting factual errors within the claim. As our primary fo- cus is on enhancing factual eπror correction through the introduction of the pivot task, leave the task of improving evidence retrieval for future research.
7 Conclusion
A Acknowledgements
this paper, we present PivotFEC, which intro- duces a pivot task, factual error injection, to im- prove factual error correction. Specifically, we first intentionally introduce factual errors into correct claims using LLMs under the few-shot setting. By doing so, we can obtain enough synthetic paired data for FEC, consisting of correct claims paired with their corresponding false claims, which will be used to train the FEC corrector. As a result, Pivot- FEC demonstrates a significant improvement over
This project is supported by National Natural Sci- ence Foundation of China (Grant No. 62202023), HKU-SCF FinTech Academy, Shenzhen-Hong Kong- MongoMacao Science and Technology Plan Project (Category C Project: TDDX22020822103537000 and Theme-based Research Scheme of RGC, Hong Kong (T35-7 10/20R... We would like thank the anonymous reviewers for their constructive and informative feedback on this work.

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Negate
claim into the T5 encoder, additional evidence does not yield significant improvements. The cor- rector is optimized using the AdamW optimizer (Loshchilov and Hutter, 2019) with a learning rate of 4e - 5, batch size 64, and a linear learn- ing rate schedule with 10% warm-up steps for 400 steps. The learning rate is selected from the set {5e 6, le 5, 2e 5, 3e 5, 4e 5,5e 5). We the maximum source length to 512 and the maximum target length 256. During training, we evaluate the model every 50 steps on the synthetic validation set and choose the checkpoint with the lowest negative log- log-likelihood (NLL) loss on the validation set.
48.8%
General Rephrase
6.1%
Specific
15. 55%
26.5%
Substitute dissimlar
Substitute similar
Figure 6: Distribution of mutation types for refuted claims of the test set.
During inference, we employ beam search de- coding with a beam width of 5 to generate revised claims for the test set.
A Distribution of Mutation Types
Figure 6 presents the distribution of mutation types for revised claims of the test set.
B.3 Supervised Models
We fine-tune the pre-trained models, BART-base and T5-base, on the FECDATA training set for 1000 steps. evaluate the model every 200 steps using the FECDATA validation set. Other parame- ters remain consistent with those of PivotFEC, as stated in Section B.2.
B Implementation Details
B.1 Evidence Retrieval
Considering that our research does not focus on improving the retrieval model, we adopt the same retrieval process as previous studies (Thorne and Vlachos, 2021: Chen et al., 2023). The retrieval module primarily consists of two steps: First, we employ pre-trained seq2seq model called GENRE (Cao et al., 2021) to predict the relevant Wikipedia pages for the input claim. Then, utilize the dense passage retrieval model DPR (Karpukhin et al., 2020) to retrieve the most relevant passages from the pages predicted by GENRE.
To implement all models, we utilize the Hug- gingFace Transformers library (Wolf et al., 2019). Additionally, all experiments are conducted on 2 NVIDIA Tesla V100 GPUs with 32 GB of memory.
C Full Few-shot Prompts for FEC
Table 6 shows the few-shot exemplars prompt of the negate mutation type for the FEC task. Table 6 and Table 7 use the same demonstrated exemplars with the only difference being the order of the original claim and mutated claim.
B.2 PivotFEC
Synthetic data for FEC. We generate synthetic FEC data using REFUTED data instances from FECDATA by solving the FEI task. Since we as- sume that correct claims are readily available, utilize only the correct claims from the REFUTED data instances and exclude their paired false claims.
D Full Few-shot Prompts for FEI
Tables 7, 8, 9, and 10 show the few-shot exemplars prompt of the negate, substitute similar, substitute dissimilar and specific mutation types for the FEI task, respectively.
To provide clarity, we construct total of 1296 validation data instances and 2000 training data instances. It's worth noting that increasing the amount training data does not yield substantial improvements, as discussed in Section 5.3.
Training and Inference. During training. we ini- tialize the PivoFFEC corrector with the T5-base model. Following previous work (Thorne and Vlachos, 2021), we input the top-2 retrieved ev- idence or gold evidence paired with the false

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Evidence:
The Lion King; The story takes place in kingdom of lions in Africa and influenced William Shakespeare Hamlet.
The Lion King; The Lion King tells the story Simba young lion who succeed his father. Mufasa King of the Pride lands; however , after Simba uncle Scar murders Mufasa, Simha manipulated into thinking he was responsible and flees into exile.
Mutated claim: The Lion King has nothing to do with lions.
Original claim: The Lion King about lions.
Evidence: Indiana Jones; Henry Walton IIdiana" Jones Jr. ( (also shortened to Indy a fictional character and protagonist of Indiana Jones franchise.
Indiana Jones; George Lucas created the character homage the action heroes 1930s film serials.
Mutated claim: Indiana Jones is real.
Original claim: Indiana Jones is fictional.
Evidence: Scott Fastwood; Scott Fastwood born Scott Clinton Reeves March 21 1986 ) is an American actor, model, and professional skydiver.
Scott Eastwood: He has also been the model for the fragrance Cool Water by Davidoff..
Mutated claim: Scott Eastwood was incapable working as model.
Original claim: Scott Eastwood worked as model.
Evidence: Akshay Kumar: Kumar is also producer and martial artist who has appeared in over hundred Hindi films. Akshay Kumar, Having done so, he has established himself as lcading contemporary actor of Hindi cinema. Akshay Kumar does not work in Hindi cinema.
Mutated claim:
Original claim: Akshay Kumar works in Hindi cinema.
Evidence: Gorillaz:
Gorillaz are an English virtual band created in 1998 by musician Damon Albarn and artist Jamie
Hewlett
Virtual band; In music, a virtual band also called virtual group cartoon group, cartoon band) is any group whose members are not corporeal musicians. but animated characters. Mutated claim: Gorillaz is German live band.
Original claim: Gorillaz is British virtual band.
Grant Gustin; Thomas Grant Gustin born January 14, 1990 ) is American actor, singer, and dancer Grant Gustin; He is known for his role Barry Allen the Flash ( based the DC Comics character of the name ) the CW series The Flash and Arrow. both in the Arrowverse television franchise, and his role Sebastian Smythe the Fox series Glee.
Evidence:
Mutated claim: Grant Gustin is only writer.
Original claim: Grant Gustin is singer.
Evidence: RB Leipzig; RasenBallsport Leipzig V., commonly known RB Leipzig is German association football club based in Leipzig. . Saxony.
Foothall Germany: Foothall the most popular sport Germany.
Mutated claim: RB Leipzig plays the least popular German sport.
Original claim: RB Leipzig plays the most popular German sport.
One World Trade Center also known 1 World Trade Center WTC or Freedom Tower is the main building the rebuilt World Trade Center complex in Lower Manhattan, New York City World Trade Center (:00--pe::::::: The original World Trade Center featured the landmark Twin Towers, which opened in 1973, and were tallest buildings in the world their completion..
Evidence: One World Trade Center:
Mutated claim: One World Trade Center opened in 1876. Original claim: One World Trade Center opened 2014.
Table 6: Few-shot exemplars prompt of the negate mutation type for the FEC task.

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Evidence:
The Lion King; The story takes place in kingdom of lions in Africa and influenced William Shakespeare Hamlet.
The Lion King; The Lion King tells the story of Simba young lion who succeed his father. Mufasa King of the Pride Lands; however , after Simba uncle Scar murders Mufasa, Simha manipulated into thinking he was responsible and flees into exile.
Original claim: The Lion King ahout lions.
Mutated claim: The Lion King has nothing to do with lions.
Evidence: Indiana Jones; Henry Walton IIdiana" Jones Jr. ( (also shortened to Indy a fictional character and
protagonist of Indiana Jones franchise.
Indiana Jones; George Lucas created the character homage the action heroes 1930s film serials.
Original claim: Indiana Jones is fictional.
Mutated claim: Indiana Jones is real.
Evidence: Scott Fastwood; Scott Fastwood born Scott Clinton Reeves March 21 1986 ) is an American actor, model, and professional skydiver.
Scott Eastwood: He has also been the model for the fragrance Cool Water by Davidoff..
Original claim: Scott Eastwood worked as model.
Mutated claim: Scott Eastwood was incapable working as model.
Evidence: Akshay Kumar: Kumar is also producer and martial artist who has appeared in over hundred Hindi films. Akshay Kumar, Having done so, he has established himself as lcading contemporary actor of Hindi cinema.
Original claim:
Akshay Kumar works in Hindi cinema.
Mutated claim: Akshay Kumar does not work in Hindi cinema.
Evidence: Gorillaz:
Gorillaz are an English virtual band created in 1998 by musician Damon Albarn and artist Jamie
Hewlett
Virtual band; In music, a virtual band also called virtual group cartoon group, cartoon band) is any group whose members are not corporeal musicians. but animated characters. Original claim: Gorillaz is British virtual band.
Mutated claim: Gorillaz is German live band.
Grant Gustin; Thomas Grant Gustin born January 14, 1990 ) is American actor, singer, and dancer Grant Gustin; He is known for his role Barry Allen the Flash ( based the DC Comics character of the name ) the CW series The Flash and Arrow. both in the Arrowverse television franchise, and his role Sebastian Smythe the Fox series Glee.
Evidence:
Original claim: Grant Gustin is singer.
Mutated claim: Grant Gustin only writer.
Evidence: RB Leipzig; RasenBallsport Leipzig V., commonly known RB Leipzig is German association football club based in Leipzig. , Saxony
Foothall Germany: Foothall the most popular sport Germany.
Original claim: RB Leipzig plays the most popular German sport.
Mutated claim: Leipzig plays the least popular German sport.
One World Trade Center also known 1 World Trade Center WTC or Freedom Tower is the main building the rebuilt World Trade Center complex in Lower Manhattan, New York City World Trade Center 20001-pre:en:): The original World Trade Center featured the landmark Twin Towers, which opened in 1973, , and were tallest buildings in the world their completion..
Evidence: One World Trade Center:
Original claim: One World Trade Center opened 2014. Mutated claim: One World Trade Center opened 1876.
Table 7: Few-shot exemplars prompt of the negate mutation type for the FEI task.

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Evidence: Notes Scandal (film): The soundtrack was composed by Philip Glass. Philip Glass; Philip Morris Glass born January 1937 American composer.
Original claim: Notes Scandal has soundtrack composed by American.
Mutated claim: Notes on Scandal has soundtrack composed by Armenian.
Evidence:
The Lion King: The Lion King 1994 American animated epic musical film, produced by Walt Disney Feature Animation and released by Walt Disney Pictures.
The Lion King: It the 32nd Disney animated feature film.
Original claim: The Lion King is film.
Mutated claim: The Lion King TV show.
Dead Man Down; Dead Man Down an 2013 American neo-noir crime thriller film written by J.H. Wyman and directed by Danish director Niels Arden Oplev... Dead Man Down; The film stars Colin Farrell, Noomi Rapace, Dominic Cooper, , and Terrence Howard and was released on March 8 2013........
Evidence:
Original claim: Dead Man Down was released in 2013.
Mutated claim: Dead Man Down was released in 2014.
Evidence:
Nick Jonas (album); It was released on November 10, 2014, by Island Records
Island Records; Island Records is a British-American record lahel that operates as a division of Universal Music Group UMG) )
Original claim: Nick Jonas was released by British-American record lahel.
Mutated claim: Nick Jonas was released by a Chinese-Mongole record label.
Evidence: Deadpool; Created by artist/writer Rob Liefeld and writer Fabian Nicieza, the character first appeared in The New Mutants # 98 cover-dated February 1991 )
Deadpool; Initially Deadpool was depicted as supervillain when he made his first appearance in The New Mutants and later in issues of X-Force. but later evolved into his more recognizable antiheroic persona. Original claim: Deadpool first appeared in The New Mutants.
Mutated claim: Deadpool first appeared in X X-Men.
Evidence:
Blue-ringed octopus; The bluc-ringed octopodes genus Hapalochlaena three octopus species that live in tide pools and coral reefs in the Pacific and Indian Oceans from Japan to Australia. Blue-ringed octopus; They recognized
of the world 's most venomous marine animals. Original claim: The blue-ringed octopus marine animal. Mutated claim: The blue-ringed octopus terrestrial animal.
Evidence:
Room (2015 film); Room is 2015 independent drama film directed by Lenny Abrahamson and written by Amma Donoghue, , based her novel of the same name
Room (novel); Room 2010 novel by Iris-Caandiian author Emma Donoghue
Original claim: Room based novel the same name.
Mutated claim: Room is based on short story of same name.
Evidence: Steve Buscemi; made his directorial debut 1996.. Trees Lounge which he also starred.
Trees Lounge; Trees Lounge is a 1996 feature film the debut of Steve Buscemi as writer and director.
Original claim: Steve Buscemi directed the film Trees Lounge.
Mutated claim:
Steve Buscemi directed the television show Trees Lounge.
Table 8: Few-shot exemplars prompt of the substitute similar mutation type for the FEI task.

---

Evidence: Kurt Angle; He then freestyle wrestling gold medal the 1996 Summer Olympics Kurt Angle; After graduating college Angle gold medal freestyle wrestling the 1995 World Wrestling Championships.
Original claim: Kurt Angle professional wrestler.
Mutated claim: Kurt Angle fish.
Evidence:
Selena Gomez; Between 2009 and 2011, Gomez starred films such as Princess Protection Program, Ramona
and Beezus, and Monte Carlo, and took a more mature role in Spring Breakers (2033) Princess Protection Program: Princess Protection Program a 2009 Disney Channel Original Movie, , directed by Allison Liddi-Brown and starring Demi Lovato and Selena Gomez. Original claim: Selena Gomez starred in Princess Protection Program.
Mutated claim: Selena Gomez reviewed Princess Protection Program.
Evidence:
Sterling Archer; Sterling Malory Archer, known simply as Archer, the titular character and the main
protagonist of the American animated comedy series Archer, Archer (TV series); Archer is an American adult animated spy sitcom created by Adam Reed for the FX network.
Original claim: Sterling Archer is the main character of a comedy series.
Mutated claim: Sterling Archer directed a comedy series.
Evidence: United States Congress; The House of Representatives has six non-voting members in addition to its 435 voting
members..
United States Congress; Congress has 535 voting members 435 Representatives and 100 Senators.
Original claim: The United States Congress has 435 Representatives.
Mutated claim: The United States Congress has 435 mineticlds.
Evidence: Carole King; Carole King ( born Carol Joan Klein, February 9, 1942) is an American composer and singer- songwriter.
Carole King; She is the most successful female songwriter of the latter half of the 20th century. having written co-written 118 pop hits on the Billboard Hot 100 between 1955 and 1999. Original claim: Carole King is American.
Mutated claim: Carolc King is acrobat.
Manatee; Manatces family Trichechidac, , genus Trichechus large, fully aquatic, mostly herhivorous marine mammals sometimes known Herbiiooe; A herbivore is an animal anatomically and physiologically adapted eating plant material. for example foliage for the main component of its diet.
Evidence:
cows
Original claim: Manatees similar to cows land.
Mutated claim: Manatees
on land.
Lion (2016 film); Lion a 2016 biographical film directed by Garth Davis ( in his feature debut and written by Luke Davies, based on the non-fiction book Long Way Home by Saroo Brierley with Larry Buttrose.
Evidence:
Garth Davis; Garth Davis an Australian television, film and commercial director, best known for directing episodes of series Top of the Lake (2013), for which he received Emmy BAFTA nominations.
Original claim: Lion was directed by Garth Davis.
Mutated claim: Lion was directed by plants.
Evidence: Tropic Thunder: Tropic Thunder 2008 satirical action comedy film co-written, produced, and directed by Ben Stiller
Tropic Thunder; was written by Stiller. Justin Theroux and Etan Cohen
Original claim: Tropic Thunder was written by Justin Theroux.
Mutated claim:
Tropic Thunder was awoken by Justin Theroux.
Table 9: Few-shot exemplars prompt of the substitute dissimilar mutation type for the FEI task.

---

Evidence: Bryan Adams; He has also won MTV ASCAP, American Music awards, three Ivor Novello Awards for song composition and has been nominated five times for Golden Globe Awards and three times for Academy Awards for his songwriting for films..
Ivor Novello Awards; The Ivor Novello Awards named after the Cardiff-bom entertainer Ivor Novello, are awards for songwriting and composing.
Original claim: Bryan Adams has won Ivor Novello Awards.
Mutated claim: Bryan Adams has won Ivor Novello Awards for his singing.
Evidence: Malcolm Young; Malcolm Mitchell Young ( born 6 January 1953 ) is an Australian retired musician and songwriter, hest known as cn-founder, rhythm guitarist, backing vocalist and songwriter for the hard rock hand AC/DC.. Malcolm Young; Except for a brief absence in 1988, he was with the band from its November 1973 beginning until retiring permannttly in 2014. due to health reasons.
Original claim: Malcolm Young co founded AC/DC in 1973.
Mutated claim: Malcolm Young co-founded AC/DC in July 1973.
Evidence: Gillian Jacobs; Jacobs has also had a recurring role Mimi-Ross Howard on the HBO series Girls and has appeared in films such Gardens of the Night 2008 ), The Lookalike 2014) ), Life Partners 2014 ), Hot Tub Time Machine 2 2015) ), Do n't Think Twice 2016) and Brother Nature 2016) ).
Hot Tub Time Machine 2; Hot Tub Time Machine 2 is 2015 American comedy film directed by Steve Pink and written hy Josh Heald.
Original claim: Gillian Jacobs appeared in the film Hot Tub Time Machine
Mutated claim: Gillian Jacobs appeared in the horror film Hot Tub Time Machine 2.
Evidenee: Mare Maron: He has been host of The Mare Maron Show and co-host of both Morning Sedition and Breakroom Live. all politically oriented shows produced by Air America Media
The Mare Maron Show; featured interviews both political and showbusiness live comedy and extensive banter between Maron and Jim Earl, Maron co-host, , who provides humorous introductions after each commercial break and plays several of the recurring characters in the show 's skits..
Original claim: Mare Maron was host of The Mare Maron Show.
Mutated claim: Mare Maron was only host of The Mare Maron Show.
Evidence:
The Offispring: The band third studio album, Smash 1994 ). became their first commercial success and has sold over eleven million copies worldwide setting record for most albums sold on an independent label and becoming the first album on Epitaph to obtain gold and platinum status,
Smash (The Offspring album); Recording and production were finished month later, , and the album was released on April 8, 1994 on Epitaph Records.
Original claim: The Offspring released Smash in 1994.
Mutated claim: The Offspring released Smash in May of 1994.
NASA; Since that time, most US space exploration efforts have been led by NASA, including the Apollo Moon landing missions. the Skylab space station, and later the Space Shuttle. Skylab; Skylab was the United States first space station. orbiting Earth from 1973 to 1979, when fell back to Earth amid huge worldwide media attention.
Evidence:
Original claim: NASA is responsible for the Skylab space station. Mutated claim: NASA is responsible for the Skylab space station, launched in 1980.
Golden State Warriors; The Warriors compete in the National Basketball Association [BA) a member of the league 's Westem Conference Pacific Division.
Evidence:
National Basketball Association; It has 30 teams (29 in the United States and in Canada) ), and is an active member of USA Basketball USAB ), which is recognized by FIBA ( also known as the International Basketball Federation as the national governing body for basketball in the United States.
Original claim: The Golden State Warriors are in the NBA.
Mutated claim: The Golden State Warriors are one of 32 teams in the NBA.
Evidence: Morrissey; Born in Davyhulme, Lancashire, working- Irish migrant family, Morrissey grew up in Manchester,
Morrissey; Steven Patrick Morrissey ( (oon 22 May 1959) ), professionally known Morrissey is an English singer, songwriter and author
Original claim: Morrissey was born into working-class family.
Mutated claim: Morrissey born into working-class family 1983.
Table 10: Few-shot exemplars prompt of the specific mutation type for the FEI task.


Reviews:
[{"review_id": "0rk7KIQSuo", "reviewer": "Reviewer_Ejt3", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your thoughtful and valuable comments. \n\n> *Q1*: Differences between our proposed method and previous methods \n\nGiven the absence of paired data (i.e., false claims and their corresponding correct claims), previous works have commonly resorted to the 'mask-then-correct' paradigm. \nThis paradigm relies solely on unpaired false claims and correct claims, thus being called  distantly supervised methods. \nOur proposed method differs from previous works in two aspects: \n\n(1) Previous methods do not train correctors on synthetic data for factual error correction. They follow the 'mask-then-correct' pipeline to revise false claims. \nConsider the false claim ''The 2013 NBA draft was held in Pennsylvani'' (Refer to Figure 1 in our paper for more details). For instance, \nthey first use a masker to identify the factual errors and then mask them. After these operations, one may get a masked claim ''The 2013 NBA draft was held in [MASK].\" Finally, they will feed the masked claim into the corrector, and expect the corrector to generate correct claims (The corrector is trained to reconstruct correct claims conditioned on masked claims and evidence). \n\n\nHowever, these methods require a masker to explicitly identify factual errors within false claims before revising with a corrector. \nGiven the absence of paired data to train the masker, accurately pinpointing factual errors within claims is non-trivial. \nConsequently, the performance of prior works suffers due to this bottleneck. \n\n(2) In this work, we propose to use LLMs to deliberately inject factual errors into correct claims. By doing so, we can obtain a substantial set of synthetic paired data, which will be used to train the corrector for factual error correction. \nOur proposed corrector operates without necessitating a masker, effectively bypassing the bottleneck associated with the explicit identification of factual errors. \n\n> *Q2*: Comparison between LLMs and rule-based methods for constructing datasets\n\nTo address the concerns raised, we implement two rule-based methods to generate synthetic paired data, and then train two factual error correctors on them. These two rule-based methods are used to create synthetic data for improving the faithfulness in text summarization (refer to https://arxiv.org/pdf/2109.09209.pdf for more details). \n\\\n(1) We first resort to spacy, a free open-source library for NLP, to recognize name entities in correct claims. \nWe then create false claims by swapping named entities from the correct claims with alternative entities of the same entity type randomly chosen from the training dataset. This method is referred to as 'SwapEntity' (this method is mentioned in section 5.1 in https://arxiv.org/pdf/2109.09209.pdf).\n\\\n(2) The second method resorts to the mask-then-fill pipeline to create false claims. \n In this approach, named entities within the correct claims are replaced with [MASK] tokens. The masked claims are then processed through the BART model to generate a new claim by filling the [MASK] tokens. These newly generated claims are considered as false claims. This method is termed 'MaskEntity' (this method is mentioned in section 5.2 in https://arxiv.org/pdf/2109.09209.pdf). \n\nSince the other two methods mentioned in sections 5.3 and 5.4 in the paper about faithful summarization (https://arxiv.org/pdf/2109.09209.pdf) rely on paired data, they are not applicable to this task. Both our work and previous works try to solve factual error corrections with the assumption that paired data is not available. \n \nThe experimental results are summarized in the subsequent table. Our proposed model, 'PivotFEC', leverages LLMs (in this table, using ChatGPT) to inject factual errors into correct claims. Results in this table indicate that our proposed method notably outperforms the rule-based methods. \nFurthermore, our proposed model achieves the peak performance with just 2000 synthetic data instances for training the corrector, whereas rule-based methods necessitate 10000 synthetic data instances to reach their peak performances for corrector training. \nThese results can be attributed to the enhanced quality of false claims produced by ChatGPT. Rule-based methods, by contrast, often produce false claims with suboptimal quality in two key aspects: 1) Grammatical errors might be present in rule-based generated false claims. 2) false claims generated by rule-based methods might deviate from the original topics present in correct claims. \nGiven the remarkable in-context learning capabilities of LLMs, factual errors can be introduced without introducing grammatical errors and deviating from the original topics.\n\nWe will add these rule-based methods and their performances to the final version of our paper. \n\n| Models/Metrics | SARI Final | ROUGE-2 |\n| ------------- | ------------- | ------------- |\n| PivotFEC  | **66.30**  | **66.68** |\n| SwapEntity  | 59.71  |62.06 |\n|MaskEntity |61.24 |63.49 |", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- The paper introduces an interesting and an effective way to generate false claim that can be used to train a factual error correction system.\n- Proposed approach is effective and achieves a new state-of-the-arts in factual error correction task.\n- The paper provides decent analysis on the proposed approach such as the effect of number of in-context examples and the size of LLMs.", "reasons_to_reject": "- Although the generated false claims using LLMs are shown to be helpful in training correction system, I have some doubts about the key difference between previous methods to generate synthetic false claims such as mask infilling or entity substitution in the previous papers. Why LLMs are more helpful in generating pivot FEC task (False Claim Injection) ? Are there any evidences that the generated false claim can better model the false claims in the real world (or the model generated claim)?", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper presents a pivot task, factual error injection that makes factual error to generate false claim. To generate false claim, the paper leverages large language models like ChatGPT by using few shot examples to the prompt. This claim is used to train a factual error correction system. Experimental results in the paper show that the factual error corrector trained with the generated dataset outputperform the original counterpart (e.g. ChatGPT).", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "qE5B4aPE7U", "reviewer": "Reviewer_PYxa", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful and valuable feedback. \n\n> *Q1*: Comparison between LLMs and rule-based methods for constructing datasets\n\nTo address the concerns raised, we implement two rule-based methods to generate synthetic paired data, and then train two factual error correctors on them. These two rule-based methods are used to create synthetic data for improving the faithfulness in text summarization (refer to https://arxiv.org/pdf/2109.09209.pdf for more details). \n\\\n(1) We first resort to spacy, a free open-source library for NLP, to recognize name entities in correct claims. \nWe then create false claims by swapping named entities from the correct claims with alternative entities of the same entity type randomly chosen from the training dataset. This method is referred to as 'SwapEntity' (this method is mentioned in section 5.1 in https://arxiv.org/pdf/2109.09209.pdf).\n\\\n(2) The second method resorts to the mask-then-fill pipeline to create false claims. \n In this approach, named entities within the correct claims are replaced with [MASK] tokens. The masked claims are then processed through the BART model to generate a new claim by filling the [MASK] tokens. These newly generated claims are considered as false claims. This method is termed 'MaskEntity' (this method is mentioned in section 5.2 in https://arxiv.org/pdf/2109.09209.pdf). \n\nSince the other two methods mentioned in sections 5.3 and 5.4 in the paper about faithful summarization (https://arxiv.org/pdf/2109.09209.pdf) rely on paired data, they are not applicable to this task. Both our work and previous works try to solve factual error corrections with the assumption that paired data is not available. \n \nThe experimental results are summarized in the subsequent table. Our proposed model, 'PivotFEC', leverages LLMs (in this table, using ChatGPT) to inject factual errors into correct claims. Results in this table indicate that our proposed method notably outperforms the rule-based methods. \nFurthermore, our proposed model achieves the peak performance with just 2000 synthetic data instances for training the corrector, whereas rule-based methods necessitate 10000 synthetic data instances to reach their peak performances for corrector training. \nThese results can be attributed to the enhanced quality of false claims produced by ChatGPT. Rule-based methods, by contrast, often produce false claims with suboptimal quality in two key aspects: 1) Grammatical errors might be present in rule-based generated false claims. 2) false claims generated by rule-based methods might deviate from the original topics present in correct claims. \nGiven the remarkable in-context learning capabilities of LLMs, factual errors can be introduced without introducing grammatical errors and deviating from the original topics.\n\nWe will add these rule-based methods and their performances to the final version of our paper. \n\n| Models/Metrics | SARI Final | ROUGE-2 |\n| ------------- | ------------- | ------------- |\n| PivotFEC  | **66.30**  | **66.68** |\n| SwapEntity  | 59.71  |62.06 |\n|MaskEntity |61.24 |63.49 |\n\n\n> *Q2*: Reproducibility\n\nAs per the OpenAI documentation available at https://platform.openai.com/docs/models/gpt-3-5, continuous updates are being made to enhance ChatGPT (referred to as gpt-3.5-turbo). However, OpenAI also maintains the snapshots of the gpt-3.5-turbo model for a certain period. In our study, we have employed gpt-3.5-turbo-0301, which captures the state of the gpt-3.5-turbo model from March 1st, 2023. This snapshot will remain accessible until June 13th, 2024, thereby ensuring the reproducibility of our research.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- The paper is clearly written and easy to follow.\n- The paper contains thorough experiments, including human evaluation, that show the strength of the proposed method as well as its weaknesses.", "reasons_to_reject": "1. Unless I missed it, but I don't see the need to use llm for generating false claims. In summarization, many works on improving factuality/faithfulness of the model and developing factuality detection metrics, have generated synthetic data (of unfaithful summaries) using simpler rule-based methods that are effective. This may also reduces the potential noise from ChatGPT as discussed in section 5.3. I am wondering how reliable chatgpt is in terms of reproducibility (given its constant updates). Some qualitative analysis would be nice on this part. ", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper attempts to address the task of factual error correction, where there is a lack of data that contains false claims. By generating synthetic data using llms and creating the pivot task of factual error injection, the proposed method outperforms existing distantly\nsupervised methods and few-shot methods. ", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "KP1hlGRUZ4", "reviewer": "Reviewer_8rV1", "summary": "", "questions": "", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful and valuable comments\n\n> *Q1*: Differences between our proposed method and previous methods \n\nGiven the absence of paired data (i.e., false claims and their corresponding correct claims), previous works have commonly resorted to the 'mask-then-correct' paradigm. \nThis paradigm relies solely on unpaired false claims and correct claims, thus being called  distantly supervised methods. \nOur proposed method differs from previous works in two aspects: \n\n(1) Previous methods do not train correctors on synthetic data for factual error correction. They follow the 'mask-then-correct' pipeline to revise false claims. \nConsider the false claim ''The 2013 NBA draft was held in Pennsylvani'' (Refer to Figure 1 in our paper for more details). For instance, \nthey first use a masker to identify the factual errors and then mask them. After these operations, one may get a masked claim ''The 2013 NBA draft was held in [MASK].\" Finally, they will feed the masked claim into the corrector, and expect the corrector to generate correct claims (The corrector is trained to reconstruct correct claims conditioned on masked claims and evidence). \n\n\nHowever, these methods require a masker to explicitly identify factual errors within false claims before revising with a corrector. \nGiven the absence of paired data to train the masker, accurately pinpointing factual errors within claims is non-trivial. \nConsequently, the performance of prior works suffers due to this bottleneck. \n\n(2) In this work, we propose to use LLMs to deliberately inject factual errors into correct claims. By doing so, we can obtain a substantial set of synthetic paired data, which will be used to train the corrector for factual error correction. \nOur proposed corrector operates without necessitating a masker, effectively bypassing the bottleneck associated with the explicit identification of factual errors. \n\n> *Q2*: Comparison between LLMs and rule-based methods for constructing datasets\n\nTo address the concerns raised, we implement two rule-based methods to generate synthetic paired data, and then train two factual error correctors on them. These two rule-based methods are used to create synthetic data for improving the faithfulness in text summarization (refer to https://arxiv.org/pdf/2109.09209.pdf for more details). \n\\\n(1) We first resort to spacy, a free open-source library for NLP, to recognize name entities in correct claims. \nWe then create false claims by swapping named entities from the correct claims with alternative entities of the same entity type randomly chosen from the training dataset. This method is referred to as 'SwapEntity' (this method is mentioned in section 5.1 in https://arxiv.org/pdf/2109.09209.pdf).\n\\\n(2) The second method resorts to the mask-then-fill pipeline to create false claims. \n In this approach, named entities within the correct claims are replaced with [MASK] tokens. The masked claims are then processed through the BART model to generate a new claim by filling the [MASK] tokens. These newly generated claims are considered as false claims. This method is termed 'MaskEntity' (this method is mentioned in section 5.2 in https://arxiv.org/pdf/2109.09209.pdf). \n\nSince the other two methods mentioned in sections 5.3 and 5.4 in the paper about faithful summarization (https://arxiv.org/pdf/2109.09209.pdf) rely on paired data, they are not applicable to this task. Both our work and previous works try to solve factual error corrections with the assumption that paired data is not available. \n \nThe experimental results are summarized in the subsequent table. Our proposed model, 'PivotFEC', leverages LLMs (in this table, using ChatGPT) to inject factual errors into correct claims. Results in this table indicate that our proposed method notably outperforms the rule-based methods. \nFurthermore, our proposed model achieves the peak performance with just 2000 synthetic data instances for training the corrector, whereas rule-based methods necessitate 10000 synthetic data instances to reach their peak performances for corrector training. \nThese results can be attributed to the enhanced quality of false claims produced by ChatGPT. Rule-based methods, by contrast, often produce false claims with suboptimal quality in two key aspects: 1) Grammatical errors might be present in rule-based generated false claims. 2) false claims generated by rule-based methods might deviate from the original topics present in correct claims. \nGiven the remarkable in-context learning capabilities of LLMs, factual errors can be introduced without introducing grammatical errors and deviating from the original topics.\n\nWe will add these rule-based methods and their performances to the final version of our paper. \n\n| Models/Metrics | SARI Final | ROUGE-2 |\n| ------------- | ------------- | ------------- |\n| PivotFEC  | **66.30**  | **66.68** |\n| SwapEntity  | 59.71  |62.06 |\n|MaskEntity |61.24 |63.49 |\n\n\n\n> *Q3*: The necessity of evidence\n\nAs shown in the second table, the mutated claim generated by MaskEntity with BART without using evidence (in this case, we use bart-base)  deviates from the original topic. By comparison, the mutated claim generated by ChatGPT with evidence (our proposed method) does not deviate from the original topic. Evidence can avoid arbitrary generation when models generate false claims, thus it is helpful to keep topics in correct claims. \n\n|     |   |\n|  ----  | ----  |\n|Evidence| Julianne Moore; From 1985 to 1988, she was a regular in the soap opera As the World Turns, earning a Daytime Emmy for her performance.|\n| Original claim  |Julianne Moore was in the television series As the World Turns.|\n| Masked claim  | \\<mask\\> was in the television series As the World Turns. | \n| Mutated claim generated by MaskEntity | The first time I saw him was in the television series As the World Turns. |\n| Mutated claim generated by ChatGPT | Julianne Moore never appeared in any television series.|\n|     |   |\n\n\n> *Q4*: The quality of the dataset being evaluated on\n\nThe dataset used in this paper is extracted from the well-known fact verification dataset, FEVER (https://aclanthology.org/N18-1074/). The authors asked annotators to introduce factual errors into correct claims with six types of mutation: paraphrasing, negation, substitution of anentity/relation with a similar/dissimilar one, and making the claim more general/specific. \nThe two most recent works have employed this dataset to evaluate factual error correction systems, and hence, we align with their settings.\n\n\nOn the other hand, to evaluate on data generated by LLMs, one needs to gather text generated by LLMs.  \nHowever, for the purpose of automatic evaluation, one also requires a reference \"gold standard\" correction. \nThus, one needs annotators to rectify factual errors in the text generated by LLMs to obtain accurate references. As far as we are aware, such a dataset is currently unavailable. Nevertheless, our focus is not on introducing a new dataset with this work. Therefore, we will leave this as a task for future endeavors.\n\nUltimately, within the table provided below, we showcase several factual errors originating from ChatGPT. These errors are related  to the concept of 'substitute similar' factual errors discussed in our paper.\n\n\n| Input  to ChatGPT| Output generated by ChatGPT | The true fact |\n| ------------- | ------------- | ------------- |\n| Please answer the question in one sentence: Which team won the 2022 NBA champion？  | The Phoenix Suns won the 2022 NBA championship. | Golden State Warriors won the 2022 NBA championship. |\n| Did Stefen curry won the NBA Finals MVP in 2015?  | Yes, Stephen Curry won the NBA Finals MVP in 2015. He played a significant role in leading the Golden State Warriors to the NBA championship that year. Curry's exceptional shooting skills and overall performance during the finals earned him the honor of being named the Finals MVP.  | Andre Iguodala won the NBA Finals MVP in 2015. |", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The proposed pipeline is easy to understand.\n2. The experiments consist of reasonable evaluations and include necessary baselines.", "reasons_to_reject": "1. The major fous of this work is proposing a pivot task “factual error injection”(FEI) that is helpful for enhancing factual error correction. Basically the authors will train a factual error corrector with synthetic data generated from LLM via FEI prompting. **However, the reviewer does not find the novelty of this proposed method** as this method does not differ from previous methods. In lines 190-195, the authors mentioned that previous methods need to do the correction by following (1) first identify factual errors within false claims; and (2) mask these errors with the FVC before using the corrector to revise them, where incorrect masking may cause a model to not perform well. However, one can use methods such as mask-and-filling to create mutated claim like what the authors want to create. For example, just randomly mask out spans (can be noun phrase, verb, etc) and then ask a model to predict the mask (see https://arxiv.org/pdf/2109.09209.pdf section 5.2 for example). One can choose words from the bottom of the beam to increase the chance that a mutated claim is wrong. In this way, in the example from figure 1: “The 2013 NBA draft was held in Los Angeles.” one can just mask “Los Angeles” or “2013” or add an extra mask before the word “held” to create all three types of wrong claims mentioned in the figure and there is even no need to refer to any evidence.\n\n2. Given that this work involves LLMs, **the reviewer has doubt on the quality of the dataset being evaluated on**. It seems like the errors from this evaluation dataset is quite obvious, such as adding negations, substitute dissimilar, etc. **Do LLMs or most recent models (the reviewer means non-LLM based here) really make these types of errors? If not, what is the use case of identifying these errors well?** It’s more interesting to see experiments with datasets that really consist of errors recent models make. Re: “Obvious errors”: the reviewer is not saying these errors are easy to detect but instead emphasize that it is quite easy to construct such synthetic data which actually current LLMs may not make at all.", "questions_to_authors": "", "paper_topic_and_main_contributions": "#############\n\nUpdate: The authors have conducted experiments to address the reviewer's concern and the results and justification seem convincing. The reviewer would like to raise the soundness score from 2 to 3.\n\n#############\n\nThis paper presents PivotFEC, a method for improving factual error correction (FEC) through a “so-called novel” pivot task named factual error injection with the help of LLMs. The authors leverage the capabilities of LLMs in few-shot learning to generate an abundance of paired data, overcoming the scarcity of such data. The method outperforms existing distantly supervised models on the FECDATA dataset, achieving new SOTA result.", "excitement": 2, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper studies the task of factual error correction - recovery of a true claim from a misinformed claim by revising the sentence to be factually accurate. The paper contributes a method for generating synthetic false claims which aid in training a factual error corrections system.  

The paper claims to aid training data sparsity by introducing factual error injection as a task to go generate the synthetic data. This approach seems well motivated. But I would also suggest that the authors compare their work to Cao et al 2020 https://aclanthology.org/2020.emnlp-main.506.pdf in the literature survey who perform artificial corruptions of summaries to train an error correction system. Much like this paper. There is value in the author's paper, especially relying on an LLM rather than a set of text transformations. 

There is reasonable consistency among the reviews for this paper, scoring 3.3 for soundness and 3 for excitement. I think the experiments provided show the the value of the methods proposed by the authors. There could be some scope for error detection and correction from LLMs in a larger context.  The authors rebutted the reviews well. However there reviewers are still concerns about novelty, and quality of generated instances that could be further addressed.

Regarding novelty, the use of the LM as a corruptor is interesting, but other methods for data corruption injection have been identified (e.g. the contrastive method of CLIFF, or the distantly supervised method of cao et al., 2020).  Comparing LLM-generated corruptions might be of interest to the community so I am not sure about the strength of this negative

Regarding quality, I would also encourage the authors to manually verify the generated instances to better understand the fluency and specificity requirements suggested by T+V2021.
Decision: Accept-Findings