| Challenge: | Existing methods for fact verification require large datasets, which can be expensive. |
| Approach: | They propose a framework for training a robust fact verification model by using automatically generated claims that can be supported, refuted, or unverifiable from evidence from Wikipedia. |
| Outcome: | The proposed framework reduces the demand for human-annotated training data and improves a model's F1 from 50% to 77%, equivalent in performance to 2K+ manually-curated examples. |
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| Challenge: | Existing methods for claim verification are inefficient or rely on external documents. |
| Approach: | They propose a step-by-step prompting-based synthetic data generation framework to enhance zero-shot claim verification. |
| Outcome: | The proposed framework bridges LLMs’ knowledge gaps in specialized domains without access to external corpora or sacrificing generalizability. |
Generating Scientific Claims for Zero-Shot Scientific Fact Checking (2022.acl-long)
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| Challenge: | Existing methods for scientific fact checking require domain expertise and time consuming. |
| Approach: | They propose a new supervised method for generating claims from scientific sentences and a novel method for negating claims. |
| Outcome: | The proposed method improves on existing methods on biomedical claims and negations. |
Unknown Claims: Generation of Fact-Checking Training Examples from Unstructured and Structured Data (2024.emnlp-main)
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| Challenge: | Existing methods for fact-checking are labor-intensive and time-consuming. |
| Approach: | They propose a framework that generates training instances for FC systems automatically using textual and tabular content. |
| Outcome: | The proposed framework generates training instances for FC systems using textual and tabular content. |
Zero-Shot Fact Verification via Natural Logic and Large Language Models (2024.findings-emnlp)
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| Challenge: | Recent advancements in fact verification systems with natural logic have enhanced their explainability by aligning claims with evidence through set-theoretic operators, providing faithful justifications. |
| Approach: | They propose a method that utilizes the generalization capabilities of instruction-tuned large language models to provide faithful justifications. |
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FEVER: a Large-scale Dataset for Fact Extraction and VERification (N18-1)
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| Challenge: | 185,445 claims generated by altering sentences from Wikipedia are verified without knowledge of the sentence they were derived from. |
| Approach: | They propose a publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. |
| Outcome: | The proposed dataset achieves 31.87% accuracy on labeling a claim accompanied by the correct evidence, compared to 50.91% if we ignore the evidence. |
Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation (2024.findings-naacl)
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| Challenge: | Knowledge-to-text generators often struggle to faithfully generate descriptions for input facts . we propose a decoding-only method to reduce hallucinations . |
| Approach: | They propose a decoding-only method to generate accurate descriptions for input facts . they use a Natural Language Inference model as the model and replace it with a task-specific HVM . |
| Outcome: | The proposed method improves faithfulness with minimal impact on quality and in/out-of-distribution evaluations. |
Zero-shot Faithful Factual Error Correction (2023.acl-long)
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| Challenge: | Using machines to correct factual errors is in high demand and requires a significant amount of human effort. |
| Approach: | They propose a zero-shot framework that asks questions about input claims and seeks correct answers from the given evidence to correct factual errors faithfully. |
| Outcome: | The proposed framework outperforms fully-supervised methods on the FEVER and SciFact datasets and is more faithful. |
Generating Fact Checking Explanations (2020.acl-main)
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| Challenge: | Existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. |
| Approach: | They propose to combine the generation of justifications for verdicts on claims with the multi-task model to optimize both objectives at the same time rather than training them separately. |
| Outcome: | The proposed model improves the informativeness, coverage and overall quality of the generated explanations, rather than training them separately. |
Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checking (2025.coling-main)
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| Challenge: | Claim matching (CM) is a binary classification task that can be used to determine if two claims can be verified using the same piece of evidence or fact-check. |
| Approach: | They propose a claim matching task that uses binary classification and large language models to test out learning approaches to the task. |
| Outcome: | The proposed task can be tackled by leveraging mature tasks such as natural language inference or paraphrase detection. |
Automated Fact-Checking of Claims from Wikipedia (2020.lrec-1)
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| Challenge: | Fact checking datasets such as FEVER and SNLI suffer from limited applicability due to synthetic nature of claims and/or evidence written by annotators that differ from real claims and evidence on the internet. |
| Approach: | They present a dataset of 124k+ triples consisting of a claim, context and an evidence document extracted from English Wikipedia articles and citations. |
| Outcome: | The proposed dataset is the largest fact checking dataset consisting of real claims and evidence to date. |