The student becomes the master: Outperforming GPT3 on Scientific Factual Error Correction
Authors: Dhananjay Ashok
Atharva Kulkarni
Hai Pham
Barnabas Poczos
Venue: EMNLP 2023 Findings (2023)
Keywords: Factual Error Correction
GPT
Domain Adaptation
Distribution Shift
TL;DR: We introduce a scientific claim correction system that makes no domain assumptions and does not require a verifier but is able to outperform existing methods by an order of magnitude

Abstract: Due to the prohibitively high cost of creating
error correction datasets, most Factual Claim
Correction methods rely on a powerful verification model to guide the correction process.
This leads to a significant drop in performance
in domains like Scientific Claim Correction,
where good verification models do not always
exist. In this work we introduce SciFix, a
claim correction system that does not require
a verifier but is able to outperform existing
methods by a considerable margin — achieving correction accuracy of 84% on the SciFact
dataset, 77% on SciFact-Open and 72.75% on
the CovidFact dataset, compared to next best
accuracies of 7.6%, 5% and 15% on the same
datasets respectively. Our method leverages the
power of prompting with LLMs during training to create a richly annotated dataset that can
be used for fully supervised training and regularization. We additionally use a claim-aware
decoding procedure to improve the quality of
corrected claims. Our method outperforms the
very LLM that was used to generate the annotated dataset — with FewShot Prompting on
GPT3.5 achieving 58%, 61% and 64% on the
respective datasets, a consistently lower correction accuracy, despite using nearly 800 times
as many parameters as our model.

The student becomes the master: Outperforming GPT3 on Scientific Factual Error Correction
Dhananjay Ashok Atharva Kulkarni Hai Pham Barnabás Póczos
(dhananja, atharvak, htpham, bapoczos @ @cs.cmu.edu
School of Computer Science, Carnegie Mellon University
Abstract
more ambitiously, automatically correct incor- rect claims would do wonders for our ability manage this risk. Researchers have identified and been working on Factual Claim Verification with some success. This is not the case, however, for Factual Error Correction, where the prohibitively high cost of manually annotating corrections of in- correct claims means there is currently no available dataset for this task (Chen et al., 2022: Thore and Vlachos, 2021). The few methods that tackle this problem use claim verification datasets for distant supervision and try to use claim verification mod- els to provide signals that can guide the correction process. This exposes the correction methods to flaws in the verification models - one of which is that current verification methods often make either an implicit or explicit domain assumption fo- cusing on news and political domains (Zeng et al., 2021; Guo et al., 2022). Due to this, today's s most potent claim verification methods fail to transfer well to scientific claims, which is especially wor- rying when we consider that scientific reports and claims are challenging for people without domain expertise to verify or correct. This also adversely impacts the best claim correction methods, with these methods struggling to perform satisfactorily on claim correction tasks from the scientific do- main.
Due the prohibitively high cost of creating error correction datasets, most Factual Cluim Correction methods rely on a powerful verifi- cation model guide the correction process. This leads to a significant drop in performance domains like scientific claims, where good verification models do not always exist. In this work, we introduce ScilFix, a scientific claim correction system that does not require ver- ifier but can outperform existing methods by a considerable margin - achieving correction accuracy of 84% the SciFact dataset, 77% on SciFact- CeiFact-Open and 72% on the CovidFact dataset, compared to next best accuracies of 7%, 5%, and 15% the same datasets re- spectively. Our method leverages the power prompting with LLMs during training to cre- ate richly annotated dataset that can be used for fully supervised training and regularization. We additionally use a claim-aware aware decoding procedure to improve the quality of corrected claims. Our method outperforms the very LLM that used generate the annotated dataset with Few-Shot Prompting on GPT3.5 achieving 58%, 61% %6 and 64% on the respective datasets, consistently lower correction accuracy, de- spite using nearly 800 times as many parame- ters our model.
In this paper, we introduce SciFFix, a Factual Claim Correction system that makes no domain assumptions, does not require a claim verification model, and shown to work well on scientific claims. Our method leverages the power of Large Language Models (LLMs) like GPT (Brown et al., 2020) to generate a claim correction dataset from claim verification dataset by corrupting 'correct' claims into 'incorrect' ones. This dataset not only allows us to learn mapping between incorrect claims and their associated correction but also al- lows us to generate rich annotations via explana- tions for why this mapping is correct. We use this
1 Introduction
The widespread adoption of the Internet has led to the distribution of more written content than ever before in human history, and recent advances in generative AI models are expected to push this trend even further (Kingma et al., 2014; Salakhut- dinov, 2015; Maaløe et al., 2016). As this decade has seen, this revolution comes with its demerits because with more content comes more inaccu- rate or false content (Balakrishnan et al., 2022: Paschen, 2020; Ozbay and Alatas, 2020). A reliable way to automatically flag these incorrect claims or,

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Training Process
Extlence neameed
Claim Generanar
Corrected
Modell
Prediction Process
Fasc Vertication Cwaabase
Prompting based Doin Ganmraliinn
Antristed Enor Correction Datalisoe
Corrected Claim
Samantic
Logts
Semanic Ofference Model
Cluim Aoiaze Decoding
Conrected Clam
Figure 1: Full Description of the SciFix system. During training fact verification database is converted well annotated error correction database using Prompting with LLMs, this database is used train Seq2Seq correction LM and a Seq2Label Semantic Difference Model. During prediction the semantic difference model helps guide the generative model using claim aware decoding generate the corrected claim
dataset to train a conditional generation language model to map evidence and incorrect claims to the appropriate correction, and the explanations serve as a useful guide during the learning process. Finally, we introduce claim-aware decoding pro- cedure to guide the generation process. While this component does not require verifier, any verifier can be easily integrated into our procedure if avail- able for the domain at hand. SciFiix can achieve 84.73% correction accuracy on the Scilacct dataset (Wadden et al., 2020a), 77.77% (Wadden et al., 2022) on the SciFact-Open dataset, and 72.75% on the CovidFact dataset, which is order of magnitude better than competing methods, with the best contemporary method achieving 7.6%, 5% and 15.45% on the datasets, respectively. More im- pressively, our method outperforms the pretrained LLMs that generated the dataset. Despite using around 800 times many parameters as our model, Few-Shot Prompting on GPT3.5 achieves 58. 78.74% 61.11%, and 64.58% the datasets, respectively, which is consistently outperformed by our method.
metrics are most closely aligned with human evaluation for error correction
Related Work
Factual Error Correction: One of the first ap- proaches for this problem was developed by Shah et al. (2020). Their strategy was to first use pre- trained fact verification model to identify and mask out the components of sentence that cause the given claim to be incorrect and then use separate model to fill in the masked sentence while remain- ing consistent with the provided evidence. Building on these ideas, Thorne and Vlachos (2021) adopts the same masking and infilling paradigm but ad- vances the choice of how to use the fact verification model in the masker and the infilling model. Most recently, Chen et al. (2022) considered fact cor- rection as an iterative sampling problem and sam- pling editing actions with respect a target density function. They also used a pre-trained verification model to guide the sampling process. These meth- ods have all made significant advances in factual error correction. However, none are expected to perform reasonably well if their verification model is poor for the task at hand. These methods work around the FEVER dataset, well-studied fact veri- fication dataset with a suite of powerful verification models that can be plugged into the method. This is not the case in the less studied sub-field of sci- entific claim verification domains (Wadden et al., 2020b, 2022), where datasets like SciFact- Open prove to be harder to tackle from a verification per- @paddine et al., 2022). ZeroFEC (Huang al., 2023) explores verifier-free. zero-shot ap- proach error correction. This method formulates questions about input claims, looks for correct an- swers in the given evidence, and assesses the faith- fulness of each correction based on its consistency
Our work presents an alternative route forward for claim correction efforts that do not rely on having access to powerful verification model and, more generally, shows that general LLMs can be used effectively as part of the model training pipeline to create more compact yet more power- ful model.
In summary, our contributions include:
1. Creation of the SciFix system perform sci- entific factual error correction,
2. Experiments showing that ScilFix outperforms current benchmarks, as well as prompting on GPT3, across multiple datasets on both syn- thetic and naturally generated 'false' claims,
3. Correlation study showing which automatic

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Evidence:
Incorrect Claim:
breast cancer patient's capacity metabolize tamoxifen does not influence treatment outcome.
Correct Claim:
breast patient's capacity to metabolize tamoxi influences treatment outcome.
Explanation:
evidence mentions that compared with extensive metabolizers, those with decreased CYP206 activity (hetorozygous xxeenivee/intermediaee metabolism) had worse event-free survival and desease-free survival, which suggests that capacity metabolize tamoxifen influences treatment outcome the claim is true.
poor
Figure 2: A sample from the generated dataset
with the evidence.
and é makes claims about similar ideas, concepts. objects, etc. as c.
Factual Consistency of Summarizations: Similar problems have arisen in making the summary of a paragraph consistent with the facts the paragraph. The relevant approaches here are the post-editing processes, which can detect and correct factual er- rors in the summary. Some methods in this domain are Cao al. (2020), Kryšciński et al. (2019) and Zhu al. (2020), which try manually introduce corruptions into correct claims and reconstruct the correct claim using a Seq2Seq model (Sutskever et al., 2014). Our method extends this approach by discarding the need for manually defined entity swapping, back translation, other intensivte methods of introducing corruptions by using an LLM provide diverse set of corrupted claims. We also provide way generate set of rich annotations that is fundamentally impossible via the corruption approaches (Cao et al., 2020; Zhang et al., 2022: Zhu et al., 2020).
In our work, we assume that we have access to a domain-specific claim verification dataset instead of correction dataset itself. Our system has three key components: i) LLM Driven Data Generation, ii) Domain Adaptive Language Modeling, and iii) Claim Aware Decoding Procedure.
LLM Driven Data Generation: First, we iden- tify something fundamental about the nature of the claim correction problem. One direction, mapping incorrect claims correct claims, is challenging since it requires deep understanding of the se- manties of both the evidence and claim to perform,
However, the reverse direction, mapping correct claims to incorrect claims, much easier and re- quires only a partial understanding of the concepts and words in the correct claim, often not requiring any evidence at all. A concrete example of this is shown in Figure 2, where it is possible generate an incorrect claim from correct claim without seeing the evidence or understanding any of the medical concepts in the sentence. However, re- cover the correct claim, one must comprehend the evidence, identify the error, and correct it. There is similar pattern in generating explanations for why a claim is valid or rewording correct claim to maintain the same meaning overall. We exploit this property using Pretrained LLM (GPT3.5): First, take the evidence and supported claims from the verification datasets and then produce a correction dataset with annotations and explana- tions for why the correct claim is true. After these steps, we create an 'augmented correct claim' (an alternate correct claim with the same meaning) for each example. A sample from this dataset is shown in Figure 2, and more are provided in the Appendix Figure 7.
Prompting with LLMs: has been shown re- cently that an LLM can achieve high performance on specific few-Shot tasks with few examples in the context window of the input (Brown al., 2020; Wei al., 2022). Chain-of-Though prompt- ing improves on simple prompting approaches by providing examples in the prompt, which contain question-anse answer pairs and some stated reasoning for the provided answer. We use these powerful methods with general LLMs to generate data for our more compact and dase-depnndnnt model.
3 Method
First, we specify how we interpret the claim veri- fication and error correction tasks. Below, we notation similar to Thorne and Vlachos (2021).
Given claim e and evidence E(c) such that the evidence contradicts c, that is, F(c) l c, our task to generate a corrected claim c'. such that d' is supported by the claim: E(c') = E(c) } c',
Domain Adaptive Language Modeling: We use Domain Adaptive Pretraining (Gururangan et al.,

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Method
SciFact
SciFact-Open
CovidFact
Agreement
ZeroFEC
6.25 + 3.44
4.0
15.45 + 4.21
0.63
VENCE
7.60 2.41
3.87
1.23 7.82
0.52
GPT ZS
54.11 + 4.92
57.73 5.22
62.33 4.80
0.56
GPT FS
58.74 2.43
61.11
7.85
64.58
3.93
0.72
SciFix Bio
87.5
2.70
80.0 + 4.21
59.25
4.74
0.80
SciFix All
84.73 + 2.71
77.77 + 7.85
72.75 + 4.13
0.80
Table I: Mean and standard deviation of correction accuracy as determined by 3 human annotators on a sample of 200 points. The SciFix (All) model outperforms all other methods on all datasets and the Bio model increases the margin on the training datasets of SciFact and SciFact-Open while dropping considerably in the hold out CovidFact dataset.
2020) on the evidence passages to give the LLM better understanding of the kinds of words and concepts that are unique to the specific domain we are interested in. Using this adapted LM, we learn a 'claim correction model', The claim correction model is trained to map the evidence and incorrect claim to a correct claim and explain why that is a correct claim. Unlike previous methods that in- terpret error correction as masking and infilling problem, interpret it conditional genera- tion task. This allows us to tackle significantly more diverse set of incorrect claims because many incorrect claims are harder correct by swapping tokens, as they may require the sentence structure to change considerably.
power of this method on scientific claim verifica- tion datasets.
Implementation Details
Datasets:
We use three different scientific domain claim verification datasets SciFact, SciFact- Open and CovidFact (Wadden et al., 2020a,b; Saakyan al., 2021). These datasets come with ev- idence passages and claims and labels for whether the claim is supported or refuted. Samples can be found in the Appendix (Figure 7). We use only the gold standard evidence paragraphs as input to all methods during training and evaluation.
Components: For the LLM Driven Data Genera- tion step, we used few-shot prompting on GPT3.5. For the Domain Adaptive Language Modeling, we trained T5-base model (Raffel et al., 2020) (220M params) on the abstracts of SciFact and SciFact-Open. After these steps, fine-tuned this model for the Claim Correction Model and Seman- tic Difference Model.
Claim Aware Decoding Procedure: Lu et al. (2021) introduced an effective way to perform con- strained decoding for generative LLMs. During beam search decoding, instead of scoring par- tial sequence as just the probability of generating that sequence tokens, the constrained decoding method performs a look-ahead search to make a greedy estimate of what the complete sequence is likely to be. Then it incorporates the goodness of the entire sequence into the score of the partial se- quence. We utilize the fact that the corrected claim should not have the same meaning the incorrect claim guide the decoding process. Specifically, we use the same adapted LM from the previous component to learn a 'semantic difference model'. The semantic difference model a classifier trained to identify when two claims are semantically simi- lar, giving score of 1 when they have different meaning and 0 when they are identical. We then use this as the scoring function for the look-ahead esti- mate, incentivizing the decoding process to avoid solutions with the same meaning as the incorrect claim.
Variation: We use the components mentioned above to train two models: the first of which is trained on all available datasets (SciFix All). For the second model (SciFFix Bio), separate datasets into two distinct 'domains'. SciFact and SeiFact-Open are biomedical datasets that use ab- stracts as their evidence passages. CovidFact a dataset from news reporting on COVID-19, with selected sentences as evidence passages. We split these into the 'biomed' and 'covid news' domains, respectively, and observe the performance when the training data is taken from the biomed domain, and the prediction task is on the covid news do- mains. This tests the ability of the methods generalize to shifting domains. All software im- plementations are publically available at: https: //gi thub. com www.///aaaannyayshoo//set.e
In the following sections, we showcase the

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Explanation | Incorrect Prediction: The evidence mentions that self-harm rates were more than ten times higher in female prisoners than in male inmates Claim: The risk of male prisoners harming themselves is 10 times that of female prisoners.
Figure 3: Explanation directly contradicts the predicted claim, signaling a mistake
Metric
ZeroFEC
VENCE
GP
GPT FS
SciFix Bio
SciFix
DiffModel
39.78
28.33
50.66
49,67
74.69
18.72
SARI -9.49
10.66
46.19
57.44
48.58
62.66
GPTScore 73.68
68.14
62.21
43.83
92.45
81.12
Annotator 86.13
87.62
71.31
87.09
95.42
86.29
Annotator 85.3
90.23
87
87.92
88.95
83.75
Annotator 87.22
84.44
87.76
84.12
95.05
87.34
Table Correlation between average correction accuracy as judged by human annotation and automatic metrics. DiffModel is the output of the Semantic Difference Model, and GPTScore is the average correction accuracy judged by few-shot prompting on GPT3.5. Results show that on all methods other than FewShot Prompting on GPT, the GPTScore metric consistently has the highest correlation with average correction accuracy
Sources:
References:
40ng/day dosage of folic acid and 2mg/day dosage of vitamin B12 does not affect chronic kidney disease (CDK) progression.
Prediction 1:
40mg/day dosage of folic acid and 2mg/day dosage of vitamin B12 has an affect on chronic kidney disease (CDK) progression.
Prediction 2:
40mg/day dosage of folic acid and 2mg/day dosage of vitamin B12 has an affect on chronic kidney disease (COK) progression.
SARI 1: 92.91
SARI 2: 75.75
Figure 4: SART metric is higher for incorrect claim than correct claim. It is unable identify semantic similarity

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5 Experiment 1: Correction Dataset
Results: Table 1 shows that SciFix consistently outperforms all other methods on all datasets, out- performing FewShot Prompting on GPT3.5 by around 16.66% (absolute) on average. The inter- annotator agreement for the methods shows that these results are relatively robust across all annota- tors. The poor performance of VENCE is possibly explained by the fact that the verifier used is not spe- cialized in scientific error verification; it is possible that if, in the future, a verifier that performs well on all of these datasets is released, it will be able to perform significantly better, however as we noted in the introduction verification methods have yet to perform as well on datasets like SeiFact-Open (Wadden et al., 2022). The ScilFix Bio model can perform better on its training domain of SciFact and SciFact-Open while not generalizing to the unseen covid domain. With a 59% accuracy on CovidFact, however, this method is sufficiently close to the performance of the GPT methods that it shows our method does have some ability to generalize across different domains.
In the first experiment, we use human annota- tion to measure the accuracy of the claim correc- tion datascts generated from the claim verification datasets. We conduct a survey of using 200 exam- ples from each model, with each example being seen by three distinct annotators. Annotators are asked to select whether the predicted claim is both 1) true given the evidence provided and 2) makes claim related to the original, incorrect claim. We compute the correction accuracy based on these re- sponses. The annotators were recruited through the personal networks of the authors and were not pro- vided any compensation for the task. To mitigate potential bias in annotation, the annotators were given no information regarding how the predicted claims were generated. Annotators were provided random samples of output from all the models with- out any indication of which model produced which output, additionally the annotators were not even aware that there could be multiple models responsi- ble for different outputs that they observed. Finally, the annotators were given no details about any of the methods they evaluated, only description of the claim correction task. The annotation task itself does not involve any graphic descriptions, disturb- ing language pertaining to sensitive topics (includ- ing but not limited hate speech, violence, sexual content etc.) or personal information. Addition- ally, all the annotators were shown examples of the task text beforehand and were informed that they were free to decline the task at any time, even after starting. More details available in the appendix (Figure 6).
Surprisingly, we find that despite never being explicitly guided to not alter input claim if it is already true, we can observe this behavior in the model. When the input to our model is the set of all the claims labeled 'SUPPORTED' in the verifi- cation datasets (i.e., already true claims), 65.82% of the time the model does not alter the input claim (i.e., string equality with the input claim which was correct), this shows our model is not simply negat- ing or flipping the meaning of input sentences but making more guided corrections to align with the evidence provided.
We use these scores to measure the correlation between human evaluation and several automatic metrics. The metrics used are (i) Semantic Differ- ence Model: the scores of the semantic difference model we trained to use for claim aware decoding. (ii) Few Shot Prompting GPT3.5: a binary indi- cator of whether GPT3.5 classifies the prediction as correct or not, see the appendix for the exact prompt used (Figure 9) and (iii) SART (Xu al., 2016), the most commonly used metric the claim correction literature, which has its origins in sum- marization.
A qualitative inspection of our model's perfor- mance shows that it can make a wide variety of edits and corrections, including swapping incorrect entities, correcting erroneous numerical figures, flipping incorrect adjectives quantifiers, as well as swapping incorrectly placed (FigeptssFigures 5, 12). However, the system's 's mistakes are equally diverse, with no clearly discernable trend in which types of examples are more complex for the model to correct appropriately (See the Appendix for ex- amples Figure 11).
Table 2 shows the correlation of the average cor- rection accuracy with automatic metrics. On SciFix ZeroFEC, and VENCE, the GPT-based metric is by far the most correlated with the average correction accuracy, nearly reaching as much correlation as the individual annotator scores themselves on cer-
Baselines: We compare our method against the fol- lowing approaches- EeroFFEC (Huang et al., 2023), VENCE (Chen et al., 2022), Zero-Shot Prompting on GPT3.5, and Few-Shot Prompting on GPT3.5 (2 examples) (Brown et al., 2020).

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tain datasets, We find that despite its widespread adoption in the task of error correction, the SARI metric not as well correlated with correction ac- curacy. This is perhaps explained by the fact that SART lacks the ability to detect semantic equiva- lence and difference as summarization metric that uses token edit distance between set of reference sentences and a prediction. We show motivating example in Figure 4, where a reasonably common example set of sentences with an extremely high SART score concerning the label can still be incor- rect answers. Similarly, a candidate with a lower SARI score can be a more appropriate answer. We report a table with the automatic metrics for each method (Table 3), and the best SART scores are achieved by our method; however, both ZeroFEC and VENCE have higher SARI scores than both GPT3.5 methods, further suggesting that this met- ric can be misleading one when used bench- mark the performance of claim correction systems, The GPT metric is not without its faults; however, as on the GPT methods, it is even less correlated with the correction than SARI and consistently overestimates the performance of the GPT-based methods while underestimating the performance of all other methods. Additionally, due to the black box nature of this metric, we have little way of understanding why this occurs and whether there is a way to mitigate it.
dard deviations of this scale, we do not try claim that any method outperforms the other based on the average correction accuracy. However, we can say that, at the very least, our method is competi- tive with both Zero-Shot and FewShot Prompting on GPT3.5 on this test set. This experiment helps verify that our method can handle incorrect claims generated through completely different process than the used to create our error correction dataset.
Ablation and Analysis
We next vary components of the pipeline to inves- tigate their individual contribution to the system's 's success. Specifically, we remove the explanations and the claim-aware decoding procedure from the pipeline. Table shows the results when we use the automatic metric most correlated with human eval- uation as judged by Table 2: correction accuracy as computed by FewShot Prompting on GPT3.5. The setting 'all components' is most consistently the best option; however, there are datasets where not having an explanation gives marginally better performance and one dataset where replacing the T5 model with BART model achieved better per- formance. The change in the decoding strategy, however, seems to have apparent adverse ef- fect the system. The setting with beam search decoding is never better than the claim-aware de- coding method. An inspection of the decoding out- put shows examples where the claim-aware decod- ing method fails because the semantic difference model is not robust to small meaningless perturba- tions. As seen in Figure 8, the prediction sentence is semantically the same as the incorrect claim. However, the semantic difference model outputs high score, implying that the two sentences have different meanings.
6 Experiment 2: Verification Dataset
In the second experiment, we measure the ability of the methods successfully correct the incorrect claims present in the claim verification dataset it- self, and this shows that our results are not only applicable to the specific correction dataset we gen- erated but are more generally applicable to incor- rect scientific claims. We use as benchmarks only the best methods from the previous experiments. Results: The results are shown in Table 4 must be interpreted cautiously; the inter-annotator agree- ment is very low at 0.24, resulting in substantial standard errors on the correction accuracy esti- mates. This is caused, in part, by the dataset not being the most appropriate for the task of error correction. There are several noisy examples, i.c., supposedly incorrect claims that are actually cor- rect, and more importantly, many examples where it is unclear whether possible to 'correct' the claim. However, it is more appropriate to restate the evidence completely (Figure 13). With stan-
The explanations show an interesting trend, with the explanation's is coherence and faithfulness being empirically correlated with the correctness of the predicted claim. This can be seen with example in Figure 3, where an explanation is incoherent, preempting incorrect prediction. More examples of explanations having this effect are provided in the Appendix Figure 10
Limitations and Future Work
SciFiix relies on data generation from LLMs whose training sets are undisclosed to the public (Brown et al., 2020). This can lead to data contamination

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SciFact
SciFact-
CovidFact
SARI
GPT
Diff
SARI
GPT
Diff
SARI
GPT
Diff
VENCE
77.48
2.27
13.01
82.61
16.69
65.62
13.66
ZeroFEC
83.82
1.13
8.15
84.66
16.66
18.49
78.32
15.09
15.12
GPT ZS
66.62
72.72
50.96
81.79
83.33
66.66
71.93
70.75
64.62
GPT FS
66.66
86.2
47.77
72.71
83.33
33.43
62.75
84.46
52.58
Our Best
94.63
77.21
92.36
94.98
80
89.99
89.77
70.27
79.97
Table 3:
Automatic Metrics on the methods. GPT is the FewShot Prompting GPT3.5 correction accuracy estimator, Diff the average output the Semantic Difference model. SARI score gives misleadingly high scores to VENCE and ZeroFEC, while the GPT score overestimates the performance of the GPT-based methods
Method
ScilFact
SciFact-Open 87.60 + 13.5
CovidFact 42.49 + 19.4
GPT ZS
68.64 + 15.39
GPT FS
74.74 + 10.7
58.57 + 14.8
63.3 + 11.66
ScilFix Bio
37.95 + 10.32
SciFFix All
80.54 = 13.58
84.84 + 8.45
65.15 + 18.22
Table 4: Correction Accuracy determined by Human Annotation the REFUTED claims from the verification datasets, high standard deviation and low inter annotation agreement (0.24 on average) indicates we can not declare any one method be superior on this task
Claim: general exercise therapy is effective than Scapular stabilizer exercises in reducing pain and improving function of the shoulder.
Proposed Correction: Scapular stabilizer exercises more effective than general exercise therapy in reducing pain and improving function of the shoulder.
Claim: Severe iemunosuppression and also cytokine storm characterize covid-19 infections Proposed Correction: Severe immunospprression and not cytokine storm characterize covid-19 infections
Claim: APOE4 expression iPSC-derived neurans increases AlphaBeta production and tau phosphorylation, delaying GABA Praposed Correction: APOE4 expression iPSC-derived tau phosphorylation, promoting GABA neuron degeneration.
degeneration.
increases AlphaBeta production and
Claim: most prevalent adverse events Semaglutide cardiovascular: Proposed Correction: The most prevalent adverse events Semaglutide are metabolic.
Claim: Coronavirus vaccine to tested on mice by june tested on humans by june
Proposed Correction: Coronavirus vaccine
Claim: Uk rolls out doses covid-19 vaccination.
Proposed Correction: Uk rolls out first doses of covid-19 vaccination
Figure 5: Examples of the claim correction system performing well diverse set of input types. The system is able to swap entities, negate only specific parts of sentence, alter numerical quantifiers word form, and more

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Ablations
SciFact
SciFact-Open
CovidFact
All Components
77.21
80
70.27
No explanations
73.41
80
70.27
Beam Decoding
70.88
80
65.45
BART Model
84.81
66.66
65.6
Table The scores stand for the percentage of correction accuracy as computed by Few Shot Prompting on GPT3.5, Ablation over system components shows that the system with all components is the best choice. The effect of the variation of components is highly dependent on the data set.
questions, where the Language Models have in some way seen the testing set. This is not a worry for the SciFact Open dataset (Wadden et al., 2022) as it was released after the update cutoff date for GPT3.5 (Sept 2021). However, it is worth noting that since our comparisons are against GPT3.5, any data contamination concerns affect those baselines far more than it affects our method.
but also outperforms Prompting on GPT3 of the largest Language Models accessible today. Factual Claim Correction and Scientific Claim Correction, in specific, are vital yet nascent sub- fields of NLP. We hope this work expands the range of possible solutions by offering effective way to perform correction without access to powerful verification model.
Our method is verifier free. However, there is considerable scope for improvement if we can de- vise a method to integrate a verifier into the method in 'soft" way, perhaps with parameter to bal- ance the tradeoff between how much the method will trust the predictions of the verifier. Addition- ally, our method does not the SUPPORTED claims from the claim verification datasets during training. Future work may study how to use this data split to improve our method efficiently. This could make the model robust to incorrect and cor- rect claims inputs. More immediately, our method could be improved by employing prompt engineer- ing to optimize the diversity and quality of the powerful Se-
10 Acknowledgment
This material is based upon work supported by the U.S. Army Research Office and the U.S. Army Futures Command under Contract No. W911NF- 20-D-0002. The content of the information does not necessarily reflect the position or the policy of the government and official endorsement should be inferred
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There are several interesting directions for future work using this method of leveraging the power of prompting to generate synthetic datasets. There are potentially several problems where the key bottle- neck has been the prohibitive cost of human notation and data creation the success of this method on Factual Error Correction suggests there may other subtasks where this method would perform well.
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9 Conclusion
In this work, we put forward SeiFix, new way utilize existing Claim Verification datasets to perform Claim Correction effectively. We show that our method is not only more powerful than existing methods on Scientific Claim Correction
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A Appendix
could volunteer their time and three people said they were available. Neither the authors nor the associates were provided direct monetary compen- sation for this task.
A.1
Details of the Human Annotation Task
For the survey had both the researchers and multiple personal associates of the researchers par- ticipate in the grading. In total used 200 ex- amples, ensuring that each example is seen by distinct annotators. The annotators were presented the evidence, incorrect claim, correct claim (ground truth), and proposed claim. The annotator is also provided with convenience indicators to show if the proposed claim is exactly equal to the correct or in- correct claim (string inequality) to make annotation easier. The annotator is asked mask this as ei- ther correct (proposed claim changes the incorrect claim into a claim that true wrt the evidence and also makes a claim about the same topics as the in- correct claim, incorrect: claim incorrect (proposed claim is either the same in meaning as the incor- rect claim or is incorrect wrt the evidence in some other way), incorrect: claim correct but unrelated to the original claim (if the proposed claim while technically true does not achieve the intended goal the claim correction task e.g. a constant output "COVID-19 a virus" should not marked as correct unless it is relevant to the incorrect claim made, this option is also to be selected for trivial answers e.g. completely restating the entire or large parts of the evidence passage). To mitigate poten- tial bias in annotation, the annotators were given information regarding how the predicted claims were generated. Annotators were provided random samples of output from all the models without any indication which model produced which output, additionally the annotators were not even aware that there could be multiple models responsible for different outputs that they observed. Finally, the annotators were given no details about any of the methods they evaluated, only description of the claim correction task. An example HIT is shown in Figure
A.3 Ethical Considerations
The annotation task itself does not involve any graphic descriptions, disturbing language pertain- ing to sensitive topics (including but not limited to hate speech, violence, sexual content etc.) or personal information. Additionally all the personal associates were shown examples of the task text beforehand and were informed that they were free to decline the task at any time, even after starting.
A.2 Recruitment
The annotators for the task were 3 authors of the pa- per and personal associates of the authors, hence 6 people overall. While the platform used to per- form the annotation was Amazon Mechanical Turk the "hiring" itself did take place through any platform, no external workers were employed and the personal associates were reached using personal networks of one of the authors. Specifically, the author asked around their friend groups if anyone

---

Incorrect Claim with Evidence:
However, specific antogenies individual populations and the overall functional organization of this celluaar network are not well defined. Here report a fate-mase mapping study the murine monocyte and macrophage compartment taking advantage constituive and conditional CX(3)CR1 promoter-dttve Cre recombinase expression. including liver Kupffer cells and lung alveolar, splenic, and peritoneal macrophages, esteblished prior birth and maintain themselves subsequently during adulthood independent of replenishment by blood monocytes. Furthermore, we have established that short-lived LyéC(+) monocytes constitute obligatory steady-stare state precursors blood booddresident Ly6C(-) cells and that the abundance of Ly6C(+) blood monocytes dynamically controls the circulation lifespan their progeny. Claim: Adult risueeresident macrophages lack a self-renewing capacity.
Evidence:
demonstrated that major reseeees macrophage populations,
Correct Claim:
Adult tissue resue-tssdeet macrophages possess self-renewing capacity.
Proposed Correction:
Microphages
capacity
macrophages.
Proposed Correction same as the incorrect claim: False
Proposed Correction same as the correct claim: False
Select an option
Correct
Incorrect: claim incorrect
Incorrect: claim correct but unrelated original claim
am not able to say
Figure 6: Example of HIT for Human annotation

---

Dataset: SciFact
Evidence:
Historically, the ribosome has been viewed as a complex ribozyme with constitutive rather
than regulatory capacity in mANA translation. Here we identify mutations of the Ribosomal Protein L38 (Rp138) gene in mice exhibiting surprising patterning defects,
including pronounced homeotic transformations of the axial skeleton. In Rp138 mutant embryos, global protein synthesis is unchanged; however, the translation of select
subset of Homeobox mRNAs is perturbed. Our data reveal that RPL38 facilitates a0s complex formation
these mŘÑAS as regulatory component of the ribosome confer transcript-specific translational control.
Incorrect Claim: Enhancement of Rp138 alters the composition of the Hox gene mRNAS translation in mice without raising overall protein synthesis.
Correct Claim: Reduction of Rol38 alters the composition of the Hox gene mRNAs translation in mice without Lowering overall protein synthesis. Explanation: The evidence explicitly mentions that Rp138 global protein synthesis unchanged,
embryos,
but the translation of select Hox mRNÁs is perturbed, thereby confirming the fact that reduction of Rp138 alters the composition of Hox gene
translation in mice thout Lowering overall protein synthesis and the claim is true.
Dataset: SciFact-Open
Evidence:
Incorrect Claim: Inhibiting glucose---phospate dehydrogenase facilitates
lipogenesis by deactivating LKB1-AMPK signaling.
Correct Claim: Inhibiting
dehydrogenase impairs lipogenesis by
reactivating LKB1-AMPK signaling.
Explanation: The evidence mentions that suppressing 6PGD decreased lipogenesis by disrupting the active LKB1 complex and reactivating
AMPK activation and that this was mediated via Ru-5-P
Dataset: CovidFact
Evidence: Meanwhile, management of the stock has been passed from a German owned multinational to an American one. The distribution of that stockpiles PPE, it seems, may no longer solely be the responsibility of Movianto.
Incorrect Claim: Private firm running uk ppe stockpile was not sold in middle of pandemic.
Correct Claim: Private firm running uk ppe stockpile was sold in middle of pandemic.
Explanation: The evidence explicitly mentions that the management of the stock was passed from
Cerman-owned multinational to American one, hence the claim is true.
Figure 7: Samples from generated dataset. The first SciFact example shows how sometimes negating multiple words can make claim that is actually not directly contradicted by the evidence due to double negatives
Incorrect Claim: First case of covid-19 reinfection? Predicted Claim: First report of covid-19 reinduction?
Figure 8: Score based decoding failing to prevent semantically equivalent solution due fragility of the semantic difference model

---

For the following evidence and claim (fact may or may not be included), declare whether
the claim is supported by the evidence and fact (TRUE) or not (FALSE):
Evidence: 'Policies requiring discontinuation of methadone in 32% of all programs contradict the evidence base
for efficacy of long-term replacement therapies and potentially result in relapse of previously stable patients
Fact: 332 of liver transplantation programs allowed patients to continue methadone treatment in 2801.'
'48% of liver transplantation programs allowed patients continue methadone treatment in 2001.'
Claim:
Answer: False
Evidence: Although disorder severity correlated with probability of treatment in almost all countries,
50.3% of serious cases in developed countries and 76.3% 85.4% less-developed countries received
treatment the 12 months before the interview.
76-85% of people with severe
mental disorder receive treatment in low and middle income countries. Claim: 3-85.4% of people with severe mental disorder receive no treatment low middle income countries.
Answer: True
Evidence: 'Policies requiring discontinuation of methadone in 32% all programs contradict the evidence base for efficacy long-term replacement therapies potentially result in relapse of previously stable patients Fact: None
Claim: Liver transplantation is risky procedure which has non trivial chance of fatal complications
Answer: False
Evidence: Although disorder severity was correlated with probability of treatment in almost all countries, 35.5% to 50.3% of serious cases in developed countries and 76. 3% to 85.4% in less-developed countries received no treatment in the 12 months before the interview. Fact: None
Claim: '35.5% to 50. 3% of people with severe mental disorder receive treatment in developed countries. Answer: True
Figure 9: Exact prompt given GPT3.5 for the Correction Accuracy as estimated by FewShot Prompting on GPT3.5

---

Explanation Incorrect Prediction: The evidence explicitly states that there no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19.
Claim: taking cadica lower fever, such as paracetamol (tylenol) and ibuprofen (advil) worsen COVID-19.
Explanation Incorrect Prediction: The evidence mentions that human coronavirus 229E was rapidly inactivated on a range of copper alloys and that Cu/Zn brasses were very effective at lower copper concentration, indicating that it can live for hours to days on surfaces like counter tops and doorknobs.
Claim: The coronovirus can live up to 24 hours to months on surfaces Like countertops or doarknicbs.
How long it survives depends on the material the surface is made from.
Explanation Incorrect Prediction: The evidence mentions that detailed analysis of multiple muscles harvested at various time points during aging showed that the muscles were of normal size, despite low regenerative capacity, but did have increased fibrosis, suggesting that lifelong reduction of satellite cells neither accelerated nor exacerbated sarcopenia. Claim: Satellite cell dysfunction is key factor in sarcopenia development
Explanation Incorrect Prediction: The evidence mentions that RmYNe2, the closest bat virus to SARS-CoV-2, is recombinant with structure that includes differential Cp6 content in Spike
Claim: Natural selection in the evolution of sars-cov-2 in cattle. not humans, created a highly capable human pathogen
Explanation Incorrect Prediction: The evidence mentions that Treg cells lacking expression integrin v8 unable suppress
pathogenic cell respanses during active inflamation, hence the claim Claim: regulatory cells (tTregs) lacking v8 more adept suppressing pathogenic T-cell responses during active inflammation.
Figure 10: Explanation directly contradicts the predicted claim, signaling mistake
Incorrect Claim: Who extends restrictions beyond easter Correct Claim: Trump extends restrictions beyond easter Proposed Correction: Who extends restrictions beyond easter
Incorrect Claim: One in two surgical randomized controlled trials are discontinued early.
Correct Claim: One in five surgical randomized controlled trials discontinued early.
Proposed Correction: One in surgical randomized controlled trials discontinued early.
Incorrect Claim: Deletion of v8 results in spontaneous inflammatrry phenotype. significant effect inflammatory phenotype. Proposed Correction: Deletion results spontaneous inflammatory phenotype.
Correct Claim: Deletion
Examples of the claim correction system performing poorly on a diverse set of input types. The system fails cases where entities must switched, quantifiers must changed and negations must be inserted
Figure 11:

---

Claim: general exercise therapy is more effective than Scapular stabilizer exercises in reducing pain and improving function
of the shoulder.
Proposed Correction: Scapular stabilizer exercises more effective than general exercise therapy in reducing pain and improving function of the shoulder.
Claim: Severe Lemunosuppression and also cytokine storm characterize covid-19 infections Proposed Correction: Severe immnosuppresssion and not cytokine storm characterize covid-19 infections
Claim: APOE4 expression iPSC-derived neurans increases AlphaBeta production and phosphorylation, delaying GABA degeneration. Proposed Correction: APOE4 expression iPSC-derived neurons increases AlphaBeta production promoting neuron degeneration.
phosphorylation,
Claim: most prevalent adverse events Semaglutide are cardiovascular.
Proposed Correction: The most prevalent adverse events Semaglutide are metabolic.
Claim: Coronavirus vaccine to tested on mice by june
Propased Correction: Coronavirus vaccine to tested on humans by june
Claim: Uk rolls out all doses of covid-19 vaccination.
Proposed Correction: Uk rolls out first doses of covid-19 vaccination
Claim: The risk of cancer is lower in individuals with history of heavy alcohol consumption
Propased Correction: The risk of cancer is higher in individuals with a history of heavy alcohol consumption.
Figure 12:
Examples of the claim correction system performing well diverse of input types. The system is able to swap entities, negate only specific parts of sentence, alter numerical quantifiers in word form, and more

---

Evidence: The Expert Working Group the Commission of Human Medicines in the UK and other organizations have stated that there is insufficient evidence establish link between ibuprofen and susceptibility
exacerbation of COVID-19.
Incorrect Claim: If Fever Helps Fight Infection, Should I Avoid Fever-Reducing Drugs?
Evidence: At the time, direct evidence existed regarding community spread this particular virus, According to one report, officials would lead a false sense security,
previous studies done in clinical settings. concerned that widespread masking
leading people to ignore other safety measures, such as handwashing
self-isolation. Incorrect Claim: Scientists continue to use common sense early the pandemic
Evidence: This clinical trial designed to assess the safety, reactogenicity and immunogenicityy of mRNA-1273 manufactured by ModernaTX, Inc. mRNA-1273 is a novel lipid nanoparticle (LMP)-encapsulatee mRNA-based vaccine that
encodes for a full-length, prefusion stabilized spike (S) protein of SARS-CoV-2.
We aimed to assess the safety and immunogenicity of an inactivated severe acute respiratory syndrome coronavirus (SARS-CoV-2) vaccine candidate, BBIBP-CorV, in humans.
Incorrect Claim: Safety and inmunogenicity study of c-ncov vaccine to prevent sars-cov-2 infection
Evidence:
thaath workers and immunosuppressed or renal patients had at greater risk of SARS-COV-2 reinfection. Incorrect Claim: Predictors of symptomatic
Evidence:
The company has already developed lab-based test thats handheld and gives results in 15 minutes.
Incorrect Claim: Researchers race to develop in-site testing
for covid-19, potential game changer hospi -confrre- sars-cov-2 reinfection
Figure 13: Difficult examples from verification datasets where is unclear what correct answer should or that the supposedly incorrect claim needs correcting all. There many such examples, making comparisons of methods noiser


Reviews:
[{"review_id": "IbMBBPdIMy", "reviewer": "Reviewer_RUuW", "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": "We thank the reviewer for their thoughtful comments. We first wanted to address the ethics concerns. The annotators for the task were 3 authors of the paper and 3 personal associates of the authors, hence 6 people overall.  While the platform used to perform the annotation was Amazon Mechanical Turk the \"hiring\" itself did not take place through any platform, no external workers were employed and the personal associates were reached using personal networks of one of the authors. Specifically, the author asked around their friend groups if anyone could volunteer their time and three people said they were available. Neither the authors nor the associates were provided direct monetary compensation for this task. After the annotation was complete the author treated the non-author annotators to dinner. The annotation task itself does not involve any graphic descriptions, disturbing language pertaining to sensitive topics (including but not limited to hate speech, violence, sexual content etc.) or personal information.  Additionally all the personal associates were shown examples of the task text beforehand and were informed that they were free to decline the task at any time, even after starting. Due to these factors and the nature of the annotation task, we felt it was appropriate to conduct the annotation without review from an IRB.\n\nWe also wanted to address the doubts regarding uniqueness of the technical contribution of the paper due to its usage of a distillation idea. This work is undoubtedly inspired by methods that use LLMs to construct synthetic datasets, however there are some unique aspects of this that we feel are unique technical contributions. These include:\n1. The identification of sentence negation and rewording as subtasks that can be reliably performed by an LLM even without a complete understanding of the evidence or claim itself. Even if one decides to construct a synthetic dataset, it is unclear whether there is a reliable way to produce high quality synthetic datapoints. This work identifies that while claim correction is hard for LLMs to perform without understanding the evidence or scientific concepts in the claim, corrupting a correct claim can be done with just a partial understanding of the words in the sentence.\n2. Rich annotation: we do not simply construct a dataset of (incorrect claim, correct claim) pairs, but we also generate explanations that prove to be useful for regularization of the learned models. This can still be considered \"distilling information from GPT-3\", but it is a very specific way to generate high quality annotated data that is not available through any other means (no equivalent dataset exists for scientific or factual error correction)\n3. Score based decoding: apart from using the dataset to perform supervised learning, we also use it to train a model that can help guide our decoding process and incorporate claim information during this process.\n\nWe adopt the general approach of using GPT-3 to extract a dataset, but do so in a specific way to solve unique problems of the Error Correction task, and to enable components (explanations, score based decoding) that are otherwise impossible to create. As such we do not believe the adoptation of distillation undermines the unique technical contributions of the paper.", "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 paper is well-written and easy to follow.\n\n2. The paper did comprehensive experiments and thorough analysis.\n\n3. The paper proposed a strong model for factual error correction. The performance of the proposed system is significantly higher than previous baselines.", "reasons_to_reject": "1. Missing annotator hiring details: Several details regarding the recruitment of human annotators are missing. Although Appendix A contains a description of the annotation task, it lacks information about the hiring process. For instance, how many annotators were hired in total? Were they recruited through Amazon Turk or other platforms? What was the wage offered? Is this annotation task approved by the IRB?\n\n2. Adoption of distillation idea: This paper has adopted the concept of distilling information from GPT-3 to construct synthetic datasets for training smaller downstream models. While this concept has been used in many previous papers across different tasks, it raises doubts about the unique technical contribution of this paper.", "questions_to_authors": "", "paper_topic_and_main_contributions": "They introduce a scientific claim correction system that makes no domain assumptions and does not require a verifier but is able to outperform existing methods by an order of magnitude. \n\n1. They create the SciFix system to perform scientific factual error correction.\n2. Experiments showing that SciFix outperforms current benchmarks, as well as prompting on GPT3, across multiple datasets on both synthetic and naturally generated ‘false’ claims.\n3. The correlation study shows which automatic metrics are most closely aligned with human evaluation for error correction.", "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": {"Justification_for_Ethical_Concerns": "This paper hired human annotators to help verify the generated claim. However, it does not contain an ethics statement section in the paper. Although in Appendix A, the authors listed a detailed description of the annotation task, it does not release any details about the hiring process. e.g., how the annotators were hired, by Amazon Turker or other platforms? Furthermore, is this annotation task approved by the institution's IRB?  Therefore, there is an ethical concern."}}, {"review_id": "DI6TlRfTSy", "reviewer": "Reviewer_LL4j", "summary": "", "questions": "The first experiments describe the accuracy of the claim correction datasets, what is the accuracy of a dataset?", "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 reviewing our paper and for your feedback. We especially appreciate the highlighting of other relevant work; we will add that to the paper and try to make the writing easier to follow. To answer the reviewer's question on what the accuracies of experiment 1 are:\n\nThe accuracy reported in Table 1 is the correction accuracy of a model on a given dataset. This is the percentage of incorrect claims from a given dataset that a model is able to properly \"correct\" as judged by human annotation. As mentioned in the evaluation subsection of section 4, a predicted claim must be supported by, or true with respect to, the evidence provided as well as make a claim that is related to the originally incorrect claim, to be considered as properly \"corrected\". The Appendix section A.1 elaborates that the specification that the new corrected claim must be related to the originally incorrect claim is to penalize models that output constant and trivially true claims hence not really correcting the specific claim it was given. \n\nFor example, if the incorrect claim is \"A breast cancer patient's capacity to metabolize tamoxifen does not influence treatment outcome\" and the associated ground truth label is \"A breast cancer patient's capacity to metabolize tamoxifen influences treatment outcome\", then if the prediction is \"breast cancer affects individuals with breasts\" it will not be marked as \"correct\" by the human annotator, but if the prediction is \"the ability of breast cancer patients to metabolize tamoxifen has an affect on treatment outcome\" it would be marked as \"correct\".", "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": "+ Work in a very relevant domain of factual claim correction\n+ Results show promising improvements over previously introduced methods.", "reasons_to_reject": "- A large part of this method boils down to synthetic data generation, which has been done many times, and has limited novelty for the community\n- Despite being a rather straightforward method (which is not a negative), the writing (presentation) seems convoluted and hard to follow", "questions_to_authors": "The first experiments describe the accuracy of the claim correction datasets, what is the accuracy of a dataset?", "paper_topic_and_main_contributions": "In this work the authors propose a new framework for factual claim correction in the context of scientific claims (limited to the bio and med domains). The work introduces three key components: 1) leveraging existing claim verification datasets and available LLMs (GPT) the authors generate a claim correction dataset by using the LLM to create incorrect claims based on correct ones, 2) domain adaptation of the claim correction model, which is based on a T5 LM, by pre-training the model on evidence passages from the claim verification/correction dataset, 3) a claim aware decoding procedure which alternates the scoring function of beam search decoding to include correctness scores for sequence completions found using look-ahead search. Authors conduct the study on 3 datasets: SciFact, SciFact-Open, and CovidFact.  The work also includes a correlation study of available automatic metrics and human annotations.", "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": {"Missing_References": "\"Evaluating the Factual Consistency of Abstractive Text Summarization\" - Kryscinski et al. is another relevant work that could be cited in the context of generating synthetic data with simple claim transformations in the context of factual correctness (verification rather than correction)."}}, {"review_id": "qskPOxJj3s", "reviewer": "Reviewer_hZmU", "summary": "", "questions": "- What is the special ability of LLMs in generating false claims? As shown in Figure 2, incorrect claim just differs in negation, which can be easily generated by using rule-based approaches. I expect some key properties such as statistics of the generated false claims generated by LLMs when I read the paper. In L373, the paper said that the proposed system can make diverse corrections as shown in Figure 4. But, I'm not sure about the type of the errors the model can fix by watching the examples in Figure 4.", "limitations": "", "rating": 3, "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": "We appreciate the helpful feedback and suggestions. Specifically we thank the reviewer for pointing out how statistical analysis of the errors would be of interest, we will try to work that into the paper. \n\nTo address the concerns highlighted, we would first like to mention that the scope of this paper was intentionally limited to Scientific Error Correction (which is why we did not show results on the FEVER dataset). The primary reason we chose to restrict the field in this way is we feel that scientific error correction is a vital domain with some special considerations (e.g. rare vocabulary, different sense of correctness when compared to the more political news setting of FEVER, lack of a strong verification method for datasets like SciFact-Open etc.) which warranted an exclusive focus. It is true that our method does not make any domain assumptions, however we preferred to keep the usage of this method on other more standard claim verification/correction datasets to a different work.\n\nTo answer the reviewers question on why LLMs are uniquely useful for generating factually incorrect claims; there are two key reasons we prefer LLMs in generating false claims.\nThe first is that many rule based correction systems are not able to handle arbitrary input claims. Specifically in the baselines we implemented, negation based systems fail when the words to be negated were irregular or the direct negation of a word is not an appropriate substitution.  For example: our NLTK rule based baseline which consisted of both entity substitution and negation was unable to find the negation for the word \"characterize\" in \"Severe immunosuppression and also a cytokine storm characterize covid-19 infections\", however our LLM method correctly replaces this with \"are not associated with\". The negation system also converted \"Satellite cell dysfunction is a key factor in sarcopenia development\" to \"Stationary cell .....\" which is not a sensible replacement). \n\nNegation systems are also prone to double or multiple negation, which can unintentionally preserve the meaning of the input claim. Generic entity replacement systems fail when the named entities are specific to the scientific domain, e.g. \"Satellite cell dysfunction is a key factor in sarcopenia development\" has no named entities of the typical (PER, LOC, ORG, MISC types). If we make additional domain assumptions (which we would like to avoid) and use a biomedical NER based substitution system we may identify \"sarcopenia\" as an entity, but there is no way to replace it with a word that ensures the incorrect claim still makes logical sense. This is important as we do not want our generated incorrect claims to be trivially false. Our NLTK rule based negation and entity replacement system was able to alter only 28% of the datapoints from our various datasets, while prompting on GPT3.5 is able to alter all of them.\n\nThe second, more important reason, is that while rule based negation and entity swapping will never be able to change the structure of the sentences, LLMs are able to completely rewrite a sentence. Recent work seems to suggest that both entity replacement and negation insertion result in simplistic generations that do not exhibit linguistic diversity. These minor alterations frequently fall short in offering adequate inductive bias to foster robustness (Khashabi et al., 2020; Huang et al., 2020; Joshi and He, 2022). For example, in our dataset, we have the claim: \"Chronic aerobic exercise alters endothelial function, impairing vasodilating mechanisms mediated by NO.\", the LLM method generated an equivalent claim: \"Endothelial function changes due to chronic aerobic exercise, which weakens the ability of NO to widen blood vessels.\" and a negated, incorrect claim: \"Endothelial function remains stable despite chronic aerobic exercise, which strengthens the capacity of NO to dilate blood vessels.\" This example shows how the LLM is able to generate equivalent and incorrect claims with greatly altered word placement and sentence structure when compared to rule based approaches. This will enable the creation of much more diverse claims, and when used for training makes it less likely that the model will overfit due to undue focus on just the few tokens or parts of a sentence that have been altered.", "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 proposes a new method to generate datasets for scientific factual error correction task by leveraging large language models for generating false claims.", "reasons_to_reject": "- The proposed method seems domain-agnostic, but the paper only focuses on scientific factual error corrections. It would be better to experiment on FEVER dataset that is more common in this area.\n- I have some doubts about the reason why the LLMs are useful for generating factually incorrect claims. I expect some explanations on the reason for such ability compared to the previous approaches such as entity replacement and negation.", "questions_to_authors": "- What is the special ability of LLMs in generating false claims? As shown in Figure 2, incorrect claim just differs in negation, which can be easily generated by using rule-based approaches. I expect some key properties such as statistics of the generated false claims generated by LLMs when I read the paper. In L373, the paper said that the proposed system can make diverse corrections as shown in Figure 4. But, I'm not sure about the type of the errors the model can fix by watching the examples in Figure 4.", "paper_topic_and_main_contributions": "The paper presents a factual error correction system SciFix, which is a specialized factual error correction system for scientific domain. To develop such system, the paper proposes a way to generate false claims by using LLMs such as ChatGPT (GPT-3.5) by corrupting the original claim through the designed prompt. By using the annotated dataset from LLMs, the paper train a factual error correction system for scientific claim. Also, the paper leverages claim verification systems into the decoding stage by adopting the previous approach. The proposed factual error correction system achieves new state-of-the-arts in scientific error correction task.", "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "- It would be better to include some analysis(e.g. statistics) on the types of the errors (may be the generated errors from LLMs) that the system can correct."}, "extra_text": {}}, {"review_id": "xoitpRmyMm", "reviewer": "Reviewer_eYY6", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "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": "We thank the reviewer for their comments.", "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": "A novel way of constructing factual error correction datasets from factual error verification counterparts.\nA novel pipeline (a language model encoder with a custom decoder) for scientific error correction.\nThe claim-aware decoding improves the correction task by ensuring a semantic difference between false and correct claims.\nThe authors conduct extensive experiments (including qualitative analysis and ablation study) to support their claims.", "reasons_to_reject": "-", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper proposes a scientific error correction pipeline based on Large Language Models (LLMs). In doing so, it generates a factual error correction dataset from a factual claim verification dataset by converting correct claims into false claims and annotating them with explanations. This claim-explanation pair is fed into GPT3.5 to create the required dataset. Then, a language model with claim-aware decoding on top (fine-tuned T5) is used for full supervision. The experimental results show that the proposed system (SciFix) outperforms the existing work by a large margin. It is even better than GPT3.5 for the factual error correction task.", "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": {}}]

Author rebuttal:

Meta-review: The paper introduces SciFix, a novel scientific error correction system optimized for the bio and med domains, utilizing Large Language Models (LLMs) GPT3.5. By leveraging factual claim verification datasets, the research creates a unique factual error correction dataset. This is achieved by transforming correct claims into false ones using LLMs and subsequently annotating them with explanations. Furthermore, a domain-adapted T5 language model is employed, which incorporates claim-aware decoding to ensure accurate error correction. Notably, this approach surpasses existing benchmarks, even outperforming GPT3.5 in factual error correction tasks. The study encompasses multiple datasets, including SciFact, SciFact-Open, and CovidFact, and delves into the correlation between automatic metrics and human evaluations.

The paper appears solid and worthy of publication. Several reviewers have mentioned that its technical novelty is marginal. Another concern raised by a reviewer and ethics char is the description of hiring annotators, but this can be addressed easily by authors.
Decision: Accept-Findings