MAPLE: Micro Analysis of Pairwise Language Evolution for Few-Shot Claim Verification (2024.findings-eacl)
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| Challenge: | Existing methods for verification of claims are limited by the availability of labeled data. |
| Approach: | They propose a method that explores the alignment between a claim and its evidence using a seq2seq model and a novel semantic measure. |
| Outcome: | The proposed method shows significant performance improvements over baselines SEED, PET and LLaMA 2 across three fact-checking datasets. |
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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. |
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Piecing It All Together: Verifying Multi-Hop Multimodal Claims (2025.coling-main)
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| Challenge: | Existing claim verification datasets often do not require systems to perform complex reasoning or effectively interpret multimodal evidence. |
| Approach: | They propose a task that requires models to reason over multiple pieces of evidence . they construct a large-scale dataset comprising 15k multi-hop claims paired with multimodal evidence - generated and refined using large language models with additional input from human feedback. |
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EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification (2023.emnlp-main)
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| Challenge: | Existing methods to augment training data with counterfactuals fail to handle multi-hop fact verification due to their incapability to preserve complex logical relationships. |
| Approach: | They propose to augment training data with counterfactuals that alter causal features of the original data by preserving logical relationships. |
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Claim Verification in the Age of Large Language Models: A Survey (2026.acl-srw)
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| Challenge: | Recent election cycles have seen a large number of false information spread across social media and news platforms. |
| Approach: | They propose a framework for automated claim verification using Large Language Models and Retrieval Augmented Generation. |
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MultiClaimNet: A Massively Multilingual Dataset of Fact-Checked Claim Clusters (2025.findings-emnlp)
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| Challenge: | a growing number of unverified claims and expanding size of fact-checked databases require alternative, more efficient solutions. |
| Approach: | They propose to group fact-checked claims into multilingual clusters to improve claim retrieval and validation. |
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HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification (2020.findings-emnlp)
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| Challenge: | a dataset for many-hop evidence extraction and fact verification challenges models to reason with information from multiple Wikipedia articles. |
| Approach: | They propose a dataset for many-hop evidence extraction and fact verification . they challenge models to extract facts from Wikipedia articles relevant to a claim . |
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SYNTHVERIFY: Enhancing Zero-Shot Claim Verification through Step-by-Step Synthetic Data Generation (2025.findings-acl)
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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. |
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ART: Adaptive Reasoning Trees for Explainable Claim Verification (2026.findings-eacl)
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Sahil Wadhwa, Himanshu Kumar, Guanqun Yang, Abbaas Alif Mohamed Nishar, Pranab Mohanty, Swapnil Shinde, Yue Wu
| Challenge: | Large Language Models (LLMs) are powerful candidates for complex decision-making, leveraging vast encoded knowledge and remarkable zero-shot abilities. |
| Approach: | They propose a hierarchical method for claim verification that uses a root claim and a pairwise tournament of its children to determine an argument's strength. |
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Fact or Fiction: Verifying Scientific Claims (2020.emnlp-main)
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David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, Hannaneh Hajishirzi
| Challenge: | SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts annotated with labels and rationales. |
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EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification (2024.findings-acl)
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| Challenge: | Existing studies on fact verification lack a high-quality dataset for explainability . existing systems lack evidence retrieval and veracity prediction, limiting the ability to verify a claim . |
| Approach: | They propose a dataset for multi-hop explainable fact verification that summarises and modifies Wikipedia documents. |
| Outcome: | The proposed dataset aims to improve the accuracy of multi-hop explainable fact verification systems. |