Papers with claim verification
Unified Dual-view Cognitive Model for Interpretable Claim Verification (2021.acl-long)
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| Challenge: | Existing studies constructing direct interactions between the claim and each single user response to capture evidence have shown remarkable success in interpretable claim verification. |
| Approach: | They propose a Dual-view model based on the views of Collective and Individual Cognition (CICD) that captures word-level semantics based . on individual cognition, they adjust the proportion between them to generate global evidence. |
| Outcome: | The proposed model is based on the views of collective and individual cognition and achieves state-of-the-art performance on three benchmark datasets. |
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. |
| Outcome: | The proposed method outperforms baseline methods on multiple datasets and shows that it is more reliable and clearer than existing methods. |
RegNLI: Detecting Online Product Misbranding through Legal and Linguistic Alignment (2026.eacl-industry)
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| Challenge: | Existing approaches to claim verification focus on keyword matching or generic text classification . misbranding involves deceptive labeling or advertising that misleads consumers about a product's nature or quality . |
| Approach: | They propose a framework that formulates misbranding detection as an inference task between product claims and regulatory provisions. |
| Outcome: | The proposed framework outperforms baselines in misbranding detection and regulation alignment metrics. |
JointCQ: Improving Factual Hallucination Detection with Joint Claim and Query Generation (2026.findings-acl)
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| Challenge: | Existing methods for detecting factual hallucinations in generated content exhibit limitations in the first two stages of the halluciation detection pipeline. |
| Approach: | They propose a joint claim-and-query generation framework that can detect factual hallucinations in generated content. |
| Outcome: | The proposed method outperforms existing methods on open-domain QA hallucination detection benchmarks. |
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. |
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. |
A DQN-based Approach to Finding Precise Evidences for Fact Verification (2021.acl-long)
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| Challenge: | Existing methods for fact verification do not target the retrieval of precise evidences. |
| Approach: | They propose a DQN-based approach to retrieval of precise evidences . they propose best thresholds for determining the true labels of computed evidences. |
| Outcome: | The proposed method improves accuracy of fact verification by reducing label bias . it can retrieve evidence consisting of the first two sentences, but it can contain unnecessary sentences . |
DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (2020.acl-main)
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| Challenge: | Recent methods to discover evidence for explainable claim verification are nontransparent and unexplained. |
| Approach: | They propose a Decision Tree-based Co-Attention model to discover evidence for explainable claim verification using neural networks. |
| Outcome: | The proposed model boosts the F1-score by more than 3.11%, 2.41% on two public datasets. |
Improving Explainable Fact-Checking with Claim-Evidence Correlations (2025.coling-main)
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| Challenge: | Existing fact-checking systems that employ large language models fail to reveal reasoning principles behind their decision-making for the claim verdict. |
| Approach: | They propose an LLM-based fact-checking system that simulates human reasoning principles . they propose a test set to evaluate the CorXFact system in real-world and closed-domain scenarios . |
| Outcome: | The proposed system outperforms four strong fact-checking baselines in claim authenticity prediction and verdict explanation. |
Table-Text Alignment: Explaining Claim Verification Against Tables in Scientific Papers (2025.findings-emnlp)
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| Challenge: | predicting the final label alone is insufficient and offers limited interpretability. |
| Approach: | They propose to reframe table–text alignment as an explanation task requiring models to identify the table cells essential for claim verification. |
| Outcome: | The proposed taxonomy improves claim verification performance and most LLMs fail to recover human-aligned rationales, suggesting that their predictions do not stem from faithful reasoning. |
FIRE: Fact-checking with Iterative Retrieval and Verification (2025.findings-naacl)
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Zhuohan Xie, Rui Xing, Yuxia Wang, Jiahui Geng, Hasan Iqbal, Dhruv Sahnan, Iryna Gurevych, Preslav Nakov
| Challenge: | Fact-checking long-form text is challenging, and breaking it down into multiple atomic claims is not cost-effective. |
| Approach: | They propose a novel agent-based framework that integrates evidence retrieval and claim verification in an iterative manner. |
| Outcome: | The proposed framework reduces large language model (LLM) costs by an average of 7.6 times and search costs by 16.5 times while retaining the same performance. |
AttentiveChecker: A Bi-Directional Attention Flow Mechanism for Fact Verification (N19-1)
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| Challenge: | Existing fact-checking datasets do not provide manual annotations for sentence-level evidence. |
| Approach: | They propose a task-agnostic pipelined system that extracts textual evidence that supports or refutes a factual claim from Wikipedia pages. |
| Outcome: | The proposed system achieves state-of-the-art results on the FEVER dataset. |
MEVER: Multi-Modal and Explainable Claim Verification with Graph-based Evidence Retrieval (2026.eacl-long)
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| Challenge: | Existing methods for verification of claims rely on textual evidence only or ignore the explainability. |
| Approach: | They propose a multi-modal reasoning model that integrates text and visual evidence for verification. |
| Outcome: | The proposed model achieves evidence retrieval, multi-modal claim verification, and explanation generation. |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation with Lightweight Specialized LLM (2025.findings-acl)
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| Challenge: | Existing verification methods rely on unstructured text corpora to break down claims . despite strong reasoning abilities, modern LLMs struggle with modular pipelines . |
| Approach: | They propose a framework that integrates knowledge graphs with LLM reasoning . they propose KGs provide structured, semantically rich representations . |
| Outcome: | The proposed framework outperforms baselines on the FactKG dataset by 9%-12% accuracy points across multiple categories. |
Distill and Align Decomposition for Enhanced Claim Verification (2026.findings-eacl)
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Jabez Magomere, Elena Kochkina, Samuel Mensah, Simerjot Kaur, Fernando Acero, Arturo Oncevay, Charese Smiley, Xiaomo Liu, Manuela Veloso
| Challenge: | Existing methods for complex claim verification struggle to align decomposition quality with verification performance. |
| Approach: | They propose a reinforcement learning approach that optimizes decomposition quality and verifier alignment using Group Relative Policy Optimization. |
| Outcome: | The proposed method outperforms prompt-based approaches and existing methods in six evaluation settings. |
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature (2025.emnlp-main)
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David Wadden, Kejian Shi, Jacob Morrison, Alan Li, Aakanksha Naik, Shruti Singh, Nitzan Barzilay, Kyle Lo, Tom Hope, Luca Soldaini, Shannon Zejiang Shen, Doug Downey, Hannaneh Hajishirzi, Arman Cohan
| Challenge: | ScIRIFF is the only entirely expert-written instruction-following dataset for scientific literature understanding . it features complex instructions with long input contexts, detailed task descriptions, and structured outputs. |
| Approach: | They present a dataset of 137K instruction-following instances for training and evaluation . they finetuned large language models using a mix of general domain and ScIRIFF instructions . |
| Outcome: | The proposed dataset shows that on nine out-of-distribution held-out tasks, the model performs better than baselines trained on general domain instructions. |
Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering (2024.emnlp-main)
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| Challenge: | Existing Knowledge Graph Question Answering (KGQA) methods focus on answering factual questions, leaving questions involving commonsense reasoning unaddressed. |
| Approach: | They propose a commonsense KGQA methodology that axiomatically surfaces commonsensical knowledge of Large Language Models and grounding every factual reasoning step on KG triples. |
| Outcome: | The proposed method outperforms existing methods and reduces instances of hallucination and reasoning errors. |
“Who said it, and Why?” Provenance for Natural Language Claims (2020.acl-main)
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| Challenge: | generating and publishing content is so easy, we are bombarded with information and are exposed to all kinds of claims. |
| Approach: | They propose a formal definition of provenance graph for a given natural language claim . they evaluate the approach using two benchmark datasets to capture provenance . |
| Outcome: | The proposed method shows initial success in capturing provenance and its effectiveness on claim verification. |
Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing claims verification models rely on annotated data, which is expensive to create at a large scale. |
| Approach: | They propose a model that can verify complex claims without annotated data . they leverage the in-context learning ability of Large Language Models to translate a claim into a First-Order-Logic clause . |
| Outcome: | The proposed model outperforms baseline models on three datasets . it performs well on the datasets, and the results are published online. |
IMRRF: Integrating Multi-Source Retrieval and Redundancy Filtering for LLM-based Fake News Detection (2025.naacl-long)
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| Challenge: | Existing methods to detect fake news rely on manual checking, which is time-consuming. |
| Approach: | They propose a model which integrates textual corpus retrieval with knowledge graph retrieval to retrieve more comprehensive evidence and a redundant information filtering strategy which minimizes the influence of irrelevant information on the LLM reasoning process. |
| Outcome: | The proposed method outperforms state-of-the-art fact-checking baselines on two challenging fact- checking datasets. |
Pelican: Correcting Hallucination in Vision-LLMs via Claim Decomposition and Program of Thought Verification (2024.emnlp-main)
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| Challenge: | Large Visual Language Models (LVLMs) suffer from hallucinations due to limited training data, lack of * Equal contribution precise grounding, and over-reliance on language priors. |
| Approach: | They propose a framework to detect and mitigate hallucinations through claim verification using program-of-thought prompting and Python code to generate a graph. |
| Outcome: | The proposed framework improves over baseline LVLMs and existing methods across several benchmarks. |
Evidence Retrieval is almost All You Need for Fact Verification (2024.findings-acl)
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| Challenge: | Existing evidence retrieval methods adopt a trivial retrieval strategy, resulting in task-irrelevant evidence and undesirable performance. |
| Approach: | They propose a framework for evidence retrieval and joint fact verification that integrates two modules. |
| Outcome: | The proposed framework improves evidence retrieval and claims verification on a FEVER dataset. |
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. |
Evidence-guided Inference for Neutralized Zero-shot Transfer (2024.lrec-main)
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| Challenge: | Existing knowledge transfer frameworks that use label skewness to neutralize biased language are costly and impractical when it comes to scarcely labeled data. |
| Approach: | They propose a neutralized Knowledge Transfer framework to equip pre-trained language models with neutralized transferability. |
| Outcome: | The proposed framework shows that it can be used to train pre-trained models with neutralized transferability . it is compared with baselines with a zero-shot cross-domain transfer setting . |
Hierarchical Evidence Set Modeling for Automated Fact Extraction and Verification (2020.emnlp-main)
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| Challenge: | Existing methods for fact extraction and verification combine all evidence sentences to produce redundant information. |
| Approach: | They propose a framework to extract evidence sets and verify a claim to be supported, refuted or not enough info . they propose to encode and attend the claim and evidence sets at different levels of hierarchy . |
| Outcome: | The proposed framework outperforms 7 state-of-the-art methods for fact extraction and verification. |
HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking (2024.lrec-main)
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| Challenge: | determining the trustworthiness of online medical content is challenging in the digital age . fact-checking is an approach to assess the veracity of factual claims . a new dataset is presented to help advance automated fact- checking . |
| Approach: | They propose a dataset that assesses the veracity of factual claims using evidence from credible sources. |
| Outcome: | The proposed dataset can be used for automated fact-checking tasks. |
EuroVerdict: A Multilingual Dataset for Verdict Generation Against Misinformation (2025.findings-acl)
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| Challenge: | a global issue that shapes public discourse shapes opinion and decision-making . many multilingual work has focused on claim verification rather than generating explanatory verdicts . |
| Approach: | They propose a multilingual dataset designed for verdict generation covering eight European languages. |
| Outcome: | The EuroVerdict dataset covers claims, manual verdicts, and supporting evidence . it is compared with other datasets in eight European languages . |
LC-Eval: A Bilingual Multi-Task Evaluation Benchmark for Long-Context Understanding (2025.findings-emnlp)
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Sheikh Jubair, Arwa Omayrah, Amal Alshammari, Alhanoof Althnian, Abdulhamed Alothaimen, Norah A. Alzahrani, Shahad D. Alzaidi, Nora Al-Twairesh, Abdulmohsen Al-Thubaity
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated sophisticated capabilities, including the ability to process and comprehend extended contexts. |
| Approach: | They propose a bilingual, multi-task evaluation benchmark designed to evaluate long-context understanding in English and Arabic. |
| Outcome: | The proposed benchmark targets context lengths ranging from 4k to over 128k tokens. |
Assessing the Reasoning Capabilities of LLMs in the context of Evidence-based Claim Verification (2025.findings-acl)
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John Dougrez-Lewis, Mahmud Elahi Akhter, Federico Ruggeri, Sebastian Löbbers, Yulan He, Maria Liakata
| Challenge: | Large Language Models (LLMs) have shown remarkable proficiency in complex tasks where reasoning capabilities are paramount. |
| Approach: | They propose a framework to break down claims into atomic reasoning types needed for verification. |
| Outcome: | The proposed framework breaks down claims into atomic reasoning types needed for verification. |
UrduFactCheck: An Agentic Fact-Checking Framework for Urdu with Evidence Boosting and Benchmarking (2025.findings-emnlp)
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Sarfraz Ahmad, Hasan Iqbal, Momina Ahsan, Numaan Naeem, Muhammad Ahsan Riaz Khan, Arham Riaz, Muhammad Arslan Manzoor, Yuxia Wang, Preslav Nakov
| Challenge: | Existing automated fact-checking systems are predominantly developed for English . Existing systems focus on claim verification, but UrduFactQA targets factuality . |
| Approach: | They propose two hand-annotated benchmarks to enable fact-checking and factual consistency evaluation in Urdu. |
| Outcome: | The proposed benchmarks are the first of their kind for Urdu and are available online. |