Papers with fact-checking
Loki: An Open-Source Tool for Fact Verification (2025.coling-demos)
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Haonan Li, Xudong Han, Hao Wang, Yuxia Wang, Minghan Wang, Rui Xing, Yilin Geng, Zenan Zhai, Preslav Nakov, Timothy Baldwin
| Challenge: | Loki is an open-source fact-checking tool designed to address the growing problem of misinformation. |
| Approach: | They propose a tool that breaks down the fact-checking task into five steps . they propose LOKI, which offers a semiautomated, human-in-the-loop approach . |
| Outcome: | a new open-source tool is designed to address the growing problem of misinformation . the tool breaks down the fact-checking task into five steps to assist human judgment . |
Unsupervised Question Answering for Fact-Checking (D19-66)
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| Challenge: | Recent Deep Learning (DL) models have achieved human-level accuracy on natural language tasks such as question-answering, natural language inference, and textual entailment. |
| Approach: | They propose an unsupervised question-answering based approach for a similar task, fact-checking. |
| Outcome: | The proposed approach achieves label accuracy of 80.2% on the development set and 80.25% on the test set. |
A Survey on Automated Fact-Checking (2022.tacl-1)
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| Challenge: | Fact-checking is an essential task in journalism due to the speed with which information and misinformation can spread in the media ecosystem. |
| Approach: | They propose to use natural language processing to automate fact-checking by identifying common concepts and defining definitions. |
| Outcome: | The proposed method can predict the veracity of claims using natural language processing, machine learning, and databases. |
Extract and Aggregate: A Novel Domain-Independent Approach to Factual Data Verification (D19-66)
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| Challenge: | Existing methods to verify information are used to verify factual data . a domain-independent fact checking system can solve the problem entirely or at the individual stages. |
| Approach: | They propose a domain-independent fact checking system that can solve the verification problem entirely or at the individual stages. |
| Outcome: | The proposed model can achieve a score on par with state-of-the-art models based on specific datasets . it can be used to verify the truth or falsity of the fact, the authors say . |
Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models (2024.findings-naacl)
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| Challenge: | Existing methods for fact-checking text generated by large language models are expensive and time-consuming. |
| Approach: | They propose a plug-and-play framework that harnesses large language models for efficient fact-checking in a few-shot manner. |
| Outcome: | The proposed framework is compared with state-of-the-art models and shows that it can be used to speed up fact-checking in a few-shot manner. |
Dis2Dis: Explaining Ambiguity in Fact-Checking (2025.findings-naacl)
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| Challenge: | Ambiguity is a linguistic tool for encoding information efficiently, yet it also causes misunderstandings and disagreements. |
| Approach: | They propose a constrained generation task for explaining ambiguous claims in fact-checking by editing them to spell out an interpretation that can be unequivocally supported by the given evidence. |
| Outcome: | The proposed model disambiguates claims 72% of the time compared to a simple copy baseline and a Large Language Model baseline. |
FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models (2025.acl-long)
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| Challenge: | Existing fact-checking evaluation methods rely on static datasets and classification metrics, which fail to evaluate justification production and uncover the nuanced limitations of LLMs. |
| Approach: | They propose a framework that adaptively and dynamically assesses LLMs’ fact-checking capabilities by incorporating justification production alongside verdict prediction. |
| Outcome: | Experiments show that the framework differentiates among state-of-the-art LLMs, providing valuable insights into model strengths and limitations in model-centric fact-checking analysis. |
CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media (2022.aacl-main)
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| Challenge: | Existing systems to automate fact-checking lack credibility in the eyes of the users. |
| Approach: | They propose to perform automatic fact-checking by verifying whether an input claim has been fact- checked by professional fact- checkers and to return back an article that explains their decision. |
| Outcome: | The proposed method improves on the CLEF’21 CheckThat! test set by two points absolute. |
Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs (2024.findings-acl)
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| Challenge: | Recent years have witnessed an explosion of Large Language Models (LLMs), with impressive performance on various NLP tasks. |
| Approach: | They propose to use image-based representations to compare LLMs' performance on table-related tasks such as question-answering and fact-checking to determine their effectiveness. |
| Outcome: | The proposed model performs better on image-based representations than on text-based models. |
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models (2025.emnlp-industry)
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| Challenge: | Large language models (LLMs) struggle with factual accuracy in knowledge-intensive domains like healthcare. |
| Approach: | They propose a framework for improving LLM factuality in medical question answering . RAFE, Fact-Check-then-RAG and Learning from Fact Check are components . |
| Outcome: | Experimental results show that LEAF outperforms Factcheck-GPT in detecting inaccuracies and corrects errors without labeling . the framework provides a scalable solution for industrial applications requiring high factuality scores. |
KGI: An Integrated Framework for Knowledge Intensive Language Tasks (2022.emnlp-demos)
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Md Faisal Mahbub Chowdhury, Michael Glass, Gaetano Rossiello, Alfio Gliozzo, Nandana Mihindukulasooriya
| Challenge: | Existing state-of-the-art retrieval augmented generation models are not available for knowledge-intensive language tasks. |
| Approach: | They propose a retrieval augmented generation system that showcases the latest state-of-the-art retrieval models on knowledge-intensive language tasks. |
| Outcome: | The proposed system is based on the core of the KGI system. |
Chronocept: Instilling a Sense of Time in Machines (2026.eacl-srw)
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| Challenge: | Human cognition is deeply intertwined with a sense of time, known as Chronoception, which allows us to judge how long facts remain valid and when knowledge becomes outdated. |
| Approach: | They propose a model that captures nuanced patterns of emergence, decay, and peak relevance using skew-normal curves fitted along semantically decomposed temporal axes. |
| Outcome: | The proposed model captures nuanced patterns of emergence, decay, and peak relevance in two datasets. |
Can Community Notes Replace Professional Fact-Checkers? (2025.acl-short)
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| Challenge: | Fact-checkers are crucial in combating misinformation on social media . however, community moderation is often employed in parallel due to the scale of misleading content shared online. |
| Approach: | They use language models to annotate Twitter/X community notes with attributes such as topic, cited sources, and whether they refute misinformation claims. |
| Outcome: | The results show that community notes cite fact-checking sources up to five times more than previously reported. |
Reasoning over Public and Private Data in Retrieval-Based Systems (2023.tacl-1)
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| Challenge: | Existing retrieval systems assume relevant corpora are fully (e.g., publicly) accessible, but users are often unwilling to expose their private data to entities hosting public data. |
| Approach: | They propose a split iterative retrieval problem involving iterating retrieval over multiple privacy scopes and propose 'concurrentQA' benchmark to test this problem. |
| Outcome: | The proposed method improves on the existing retrieval methods but still suffers performance degradations when applied to a dataset from a public and private distribution. |
ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild (2025.coling-industry)
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| Challenge: | Existing methods for chart understanding and reasoning are weakly aligned and rely on underlying data tables. |
| Approach: | They propose a chart-based understanding and reasoning model that is trained on instruction-tuning data generated directly from chart images. |
| Outcome: | The proposed model achieves state-of-the-art results across 5 benchmarks spanning chart summarization, question answering, and fact-checking. |
REMATCH: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity (2024.findings-naacl)
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| Challenge: | Existing AMR metrics are inefficient and struggle to capture semantic similarity . Existing metrics are not efficient and lack a systematic evaluation benchmark . |
| Approach: | They propose a new AMR similarity metric, rematch, which matches graphs structurally and semantically to each other. |
| Outcome: | The proposed metric is five times faster than the next most efficient metric. |
X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across Paragraphs (2024.naacl-long)
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| Challenge: | X-parade is the first cross-lingual dataset of paragraph-level information divergences and entailments . ability to recognize differences in meaning underlies many NLP tasks . |
| Approach: | They propose a cross-lingual dataset of paragraph-level information divergences . they use a dictionary to identify new or inferred information in Wikipedia pages . |
| Outcome: | The proposed dataset shows that the proposed methods fail to handle inferable information . the dataset contains fine-grained span-level annotations for content in different languages . |
CodeGenWrangler: Data Wrangling task automation using Code-Generating Models (2025.naacl-industry)
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| Challenge: | Tabular datasets in industrial settings often encompass extensive data with numerous rows and columns. |
| Approach: | They propose a system that leverages large language models to generate executable code for data wrangling tasks . they identify inherent patterns in the data while leveraging external knowledge . |
| Outcome: | The proposed system detects patterns in the data while leveraging external knowledge . it generates executable code for data-wrangling tasks like missing value imputation and error correction . |
Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning? (2025.acl-industry)
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Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Ridwan Mahbub, Ahmed Masry, Mizanur Rahman, Amran Bhuiyan, Mir Tafseer Nayeem, Shafiq Joty, Enamul Hoque, Jimmy Huang
| Challenge: | Large Vision-Language Models (LVLMs) are expensive and time-consuming to evaluate . however, they are limited in their use in industrial settings due to their limited availability and limited resources. |
| Approach: | They evaluate 13 open-source LVLMs as judges for diverse chart comprehension and reasoning tasks. |
| Outcome: | The proposed models can be used to assess chart comprehension and reasoning tasks, but they are expensive and time-consuming. |
CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval (2022.coling-1)
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| Challenge: | Existing fact-checking approaches focus on claims made in English due to data scarcity issue in other languages. |
| Approach: | They propose a fact-checking framework augmented with cross-lingual retrieval that aggregates evidence retrieved from multiple languages through a cross-linguistic retriever. |
| Outcome: | The proposed framework achieves 2.23% absolute F1 improvement over previous systems on a X-Fact dataset. |
When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms (2026.acl-long)
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| Challenge: | Existing fact-checking pipelines focus on written claims, not on audio . authors argue that audio misinformation is structurally different because it is both spoken and conversational . |
| Approach: | They argue that audio misinformation is structurally different because it is both spoken and conversational . they argue that advancing fact-checking requires rethinking verification pipelines around spoken and conversations . |
| Outcome: | The proposed method fails on audio because it is both spoken and conversational . podcasts exceed 4.3 million distinct shows, reaching an estimated 500 million listeners globally . |
A Survey on Stance Detection for Mis- and Disinformation Identification (2022.findings-naacl)
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| Challenge: | Understanding attitudes expressed in texts plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentional false information). |
| Approach: | They examine the relationship between stance detection and mis- and disinformation detection online and examine the results of previous studies. |
| Outcome: | The proposed task is a component of fact-checking, rumour detection, and detecting previously fact- checked claims, and is compared with other related tasks such as argumentation mining and sentiment analysis. |
Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output (2024.emnlp-industry)
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| Challenge: | Existing methods for fact checking RAG outputs rely on large language models. |
| Approach: | They propose a method that computes a factuality score that can be thresholded to yield a binary decision to check RAG outputs. |
| Outcome: | The proposed method is low latency and low cost at run-time and no need for LLM fine-tuning. |
CORRECT: Context- and Reference-Augmented Reasoning and Prompting for Fact-Checking (2025.naacl-long)
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| Challenge: | Existing fact-checking models focus on reasoning within evidence sentences, but they ignore auxiliary contexts and references. |
| Approach: | They propose a method to verify the truthfulness of claims using evidence . they construct a three-layer evidence graph with evidence, context, and reference layers . |
| Outcome: | The proposed method can verify the truthfulness of claims using evidence . it integrates evidence, context, and reference layers into a unified embedding . |
Towards Few-shot Fact-Checking via Perplexity (2021.naacl-main)
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| Challenge: | Recent studies have shown that pre-trained language models can perform few-shot learning for various downstream tasks, such as question answering and machine translation. |
| Approach: | They propose a method to leverage the powerful transfer learning ability of a language model via a perplexity score to learn few-shot for the fact-checking task. |
| Outcome: | The proposed method outperforms the Major Class baseline by 10% on the F1-Macro metric across multiple datasets. |
FactCorp: A Corpus of Dutch Fact-checks and its Multiple Usages (2020.lrec-1)
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| Challenge: | Fact-checking information before publication has long been a core task for journalists, but recent times have seen the emergence of dedicated news items specifically aimed at fact-checks after publication. |
| Approach: | They propose to study fact-checks from a corpus linguistic perspective and to create a textual corpus that contains 1,974 fact- checks from three major Dutch newspapers. |
| Outcome: | The proposed method can be applied to scientific communication landscapes and to the media. |
Ask To The Point: Open-Domain Entity-Centric Question Generation (2023.findings-emnlp)
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| Challenge: | *entity-centric question generation (ECQG) is a task motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking. |
| Approach: | They propose a PLM-based framework GenCONE with two modules: content focusing and question verification. |
| Outcome: | The proposed framework outperforms baselines and is effective and complementary in generating high-quality questions. |
Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning (2024.findings-emnlp)
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Mohammed Saidul Islam, Raian Rahman, Ahmed Masry, Md Tahmid Rahman Laskar, Mir Tafseer Nayeem, Enamul Hoque
| Challenge: | Recent studies have demonstrated that large vision language models (LVLMs) are not multi-modal and lack multi-tasking capabilities. |
| Approach: | They evaluate the performance of large vision language models (LVLMs) for chart understanding and reasoning tasks and compare them to open-source models. |
| Outcome: | The proposed models demonstrate impressive abilities in generating fluent texts covering high-level data insights, but they also encounter common problems like hallucinations, factual errors, and data bias. |
Molecular Facts: Desiderata for Decontextualization in LLM Fact Verification (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) are increasingly used to combat hallucinations . granularity of fact-checking makes it difficult to fact- check larger chunks of text . |
| Approach: | They propose a method for generating molecular facts automatically using decontextuality and minimality. |
| Outcome: | The proposed method balances minimality with fact verification accuracy in ambiguous settings. |
DialFact: A Benchmark for Fact-Checking in Dialogue (2022.acl-long)
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| Challenge: | Existing fact-checking models trained on non-dialogue data fail to perform well on this task. |
| Approach: | They propose a task of fact-checking in dialogue to improve fact- checking performance . they propose to use an annotated conversational claim and Wikipedia snippets as evidence . |
| Outcome: | The proposed task improves fact-checking performance in dialogue. |
Modeling Factual Claims with Semantic Frames (2020.lrec-1)
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| Challenge: | In recent years, the proliferation of misinformation has reached a staggering pace eroding people's confidence in politics and even affected democracies. |
| Approach: | They propose an extension of the Berkeley FrameNet for the structured and semantic modeling of factual claims. |
| Outcome: | The proposed extension provides 2,540 fully annotated sentences and can be used to understand how these frames are intended to work and to train machine learning models. |
CANDY: Benchmarking LLMs’ Limitations and Assistive Potential in Chinese Misinformation Fact-Checking (2025.findings-emnlp)
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| Challenge: | CANDY is a benchmark to evaluate the capabilities and limitations of large language models (LLMs) for fact-checking misinformation. |
| Approach: | a team of researchers develop a benchmark to evaluate the capabilities and limitations of large language models in fact-checking misinformation in Chinese. |
| Outcome: | CANDY is a benchmark to evaluate the capabilities and limitations of large language models in fact-checking misinformation in China. |
Automatic Evaluation of Attribution by Large Language Models (2023.findings-emnlp)
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| Challenge: | Generative large language models (LLMs) incorporate external references to generate and support claims. however, evaluating the attribution remains an open problem. |
| Approach: | They investigate automatic evaluation of attribution given by large language models . they define different types of attributed errors and then explore two approaches . |
| Outcome: | The proposed methods highlight promising signals and challenges. |
Multimodal Fact-Checking with Vision Language Models: A Probing Classifier based Solution with Embedding Strategies (2025.coling-main)
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| Challenge: | Existing fact-checking systems that use text and image information are susceptible to fake news spread by social media platforms. |
| Approach: | They propose a neural probing classifier based on multimodality and embeddings from text and image encoders to represent multimodal content for fact-checking. |
| Outcome: | The proposed classifier outperforms KNN and SVM baselines in leveraging extracted embeddings, highlighting its effectiveness for multimodal fact-checking. |
PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training (2022.emnlp-main)
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| Challenge: | Table-based fact verification has attracted a lot of attention recently due to the lack of datasets that can be used to pre-train language models to be aware of common table operations. |
| Approach: | They propose a table-based fact verification tool that pre-trains language models to be aware of common table operations such as aggregating a column or comparing tuples. |
| Outcome: | The proposed method outperforms previous methods on two table-based fact verification datasets TabFact and SEM-TAB- FACTS. |
Reliability Estimation of News Media Sources: Birds of a Feather Flock Together (2024.naacl-long)
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| Challenge: | Recent research has shown that predicting sources’ reliability is an important first-prior step in addressing additional challenges such as fake news detection and fact-checking. |
| Approach: | They propose a method that leverages reinforcement learning strategies to estimate the reliability degree of news sources based on how all the news media sources interact with each other on the Web. |
| Outcome: | The proposed method can predict reliability labels on a large news media reliability dataset. |
Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation (2022.emnlp-main)
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| Challenge: | Existing NLP task definitions for fact-checking cannot refute misinformation as professional fact- checkers do for the majority of claims. |
| Approach: | They compare NLP-based fact-checking with professional fact- checkers . they find that evidence must be sufficient to refute the claim and not leaked . |
| Outcome: | The proposed models fail to meet the criteria for realistic fact-checking . they also fail to satisfy the criteria of leaked evidence . |
WiCE: Real-World Entailment for Claims in Wikipedia (2023.emnlp-main)
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| Challenge: | Textual entailment models are increasingly used in fact-checking, presupposition verification in question answering, or summary evaluation. |
| Approach: | They propose a new fine-grained textual entailment dataset built on natural claim and evidence pairs extracted from Wikipedia that provides en-tailment judgments over sub-sentence units of the claim and a minimal subset of evidence sentences that support each subclaim. |
| Outcome: | The proposed dataset improves on multiple datasets at test time and shows that real claims involve verification and retrieval problems that existing models fail to address. |
MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checking (2024.acl-long)
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| Challenge: | Fact-checking claims on social media platforms poses a significant challenge due to the large volume of new claims constantly being posted without sufficient methods for verification. |
| Approach: | They propose a model that generates claim-specific summaries from multimodal multi-document datasets using a perceiver-based model that is able to handle inputs from multiple modalities of arbitrary lengths. |
| Outcome: | The proposed model outperforms the SOTA approach by 4.6% in the claim verification task on the MOCHEG dataset and shows strong performance on the new multi-document claims dataset. |
bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark (2023.acl-long)
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Momchil Hardalov, Pepa Atanasova, Todor Mihaylov, Galia Angelova, Kiril Simov, Petya Osenova, Veselin Stoyanov, Ivan Koychev, Preslav Nakov, Dragomir Radev
| Challenge: | bgGLUE is a benchmark for evaluating language models on natural language understanding (NLU) tasks in Bulgarian. |
| Approach: | They propose to use a benchmark to evaluate language models on NLU tasks in Bulgarian. |
| Outcome: | The proposed model performs well on sequence labeling tasks, but there is room for improvement for tasks that require more complex reasoning. |
MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents (2024.emnlp-main)
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| Challenge: | Current methods for fact-checking are based on verifying each piece of a model against potential evidence using an LLM. |
| Approach: | They propose a method that builds small fact-checking models that have GPT-4-level performance but 400x lower cost. |
| Outcome: | The proposed model outperforms other models and reaches GPT-4 accuracy. |
Editing Factual Knowledge in Language Models (2021.emnlp-main)
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| Challenge: | KnowledgeEditor can be used to edit factual knowledge stored in Language Models without the need for expensive retraining or fine-tuning. |
| Approach: | They propose a method which edits factual knowledge implicitly stored in Language Models and uses it to fix 'bugs' and 'obvious errors' they train a hyper-network with constrained optimization to modify a fact without affecting the rest of the knowledge; the hyper-netzwork is then used to predict the weight update at test time. |
| Outcome: | The proposed method can be used to edit factual knowledge without retraining or fine-tuning and can fix 'bugs' or unexpected predictions without the need for expensive re-training or meta-learning. |
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. |
| Outcome: | The proposed approach reduces redundancy by grouping claims into clusters . the proposed dataset contains 85.3K fact-checked claims written in 78 languages . |
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News (2020.emnlp-main)
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| Challenge: | fabricated stories and hoaxes are still pervading our cyberspace. |
| Approach: | They propose a framework to search for fact-checking articles that address the content of an original tweet that may contain misinformation posted by online users. |
| Outcome: | The proposed framework can detect and disseminate fake news on real-world datasets and warn fake news posters and online users about misinformation. |
Document-level Claim Extraction and Decontextualisation for Fact-Checking (2024.acl-long)
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| Challenge: | Existing methods for document-level claim extraction focus on identifying and extracting claims from individual sentences. |
| Approach: | They propose a method for document-level claim extraction for fact-checking which aims to extract check-worthy claims from documents and decontextualise them so they can be understood out of context. |
| Outcome: | The proposed method extracts check-worthy claims from documents and decontextualises them so they can be understood out of context. |
PerCQA: Persian Community Question Answering Dataset (2022.lrec-1)
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| Challenge: | Community Question Answering (CQA) forums provide answers to many real-life questions. |
| Approach: | They propose to make Persian dataset PerCQA public to encourage more research in Persian CQA. |
| Outcome: | The proposed dataset contains 989 questions and 21,915 annotated answers from the most well-known Persian forum. |
Task-Oriented Automatic Fact-Checking with Frame-Semantics (2025.findings-acl)
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| Challenge: | Existing work on automatic fact-checking relies on unstructured data and large language models to produce fact- check verdicts and explanations. |
| Approach: | They propose a new paradigm for automatic fact-checking that leverages frame semantics to enhance the structured understanding of claims and guide the process of fact- checking them. |
| Outcome: | The proposed paradigm improves evidence retrieval and explainability for fact-checking by leveraging frame semantics. |
GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking (2025.acl-long)
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Yingjian Chen, Haoran Liu, Yinhong Liu, Jinxiang Xie, Rui Yang, Han Yuan, Yanran Fu, Peng Yuan Zhou, Qingyu Chen, James Caverlee, Irene Li
| Challenge: | Existing fact-checking methods that use large language models often generate subtle factual errors. |
| Approach: | They propose a fact-checking framework that uses extracted knowledge graphs to enhance text representation. |
| Outcome: | GraphCheck outperforms existing specialized fact-checkers on seven benchmarks spanning general and medical domains . Graph Neural Networks process extracted knowledge graphs as a soft prompt, enabling efficient fact- checking in a single inference call. |
ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media (2025.coling-main)
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| Challenge: | Existing studies have focused on the identification of social media posts that contain misrepresentations of information within associated news articles. |
| Approach: | They propose a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles. |
| Outcome: | The proposed model outperforms large language models on the ManiTweet dataset and reveals intriguing connections between manipulation and the domain and factuality of news articles. |
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking (2020.acl-main)
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Christopher Hidey, Tuhin Chakrabarty, Tariq Alhindi, Siddharth Varia, Kriste Krstovski, Mona Diab, Smaranda Muresan
| Challenge: | Fact Extraction and Verification datasets provide a resource for end-to-end fact-checking, requiring retrieval of evidence from Wikipedia to validate a veracity prediction. |
| Approach: | They propose a system that is resilient to attacks by multiple propositions, temporal reasoning, ambiguity and lexical variation and a sequence of evidence sentences and veracity relation predictions. |
| Outcome: | The proposed system is resilient to three realistic “attacks” and obtains state-of-the-art results due to improved evidence retrieval. |
Explaining Interactions Between Text Spans (2023.emnlp-main)
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| Challenge: | Existing highlight-based explanations focus on identifying individual important features or interactions only between adjacent tokens or tuples of tokens. |
| Approach: | They propose a multi-annotator dataset of human span interaction explanations for NLU and FC. |
| Outcome: | The proposed method compares human reasoning processes to those of a fine-tuned large language model. |
ECIS-VQG: Generation of Entity-centric Information-seeking Questions from Videos (2024.emnlp-main)
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| Challenge: | Existing studies on question generation from videos are mostly focused on generating questions about common objects and attributes. |
| Approach: | They propose a model architecture combining Transformers, rich context signals and a combination of cross-entropy and contrastive loss function to encourage entity-centric question generation. |
| Outcome: | The proposed system yields BLEU, ROUGE, CIDEr, and METEOR scores of 71.3, 78.6, 7.31, and 81.9. |
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers (2024.findings-emnlp)
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Yuxia Wang, Revanth Gangi Reddy, Zain Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, Preslav Nakov
| Challenge: | Large language models generate naturally sounding answers over a broad range of human inquiries, but they often generate answers that contradict real-world facts. |
| Approach: | They propose a framework for annotating and evaluating the factuality of large language models . they propose 'factcheck-bench' which provides a multi-stage annotation scheme . |
| Outcome: | The proposed framework outperforms several popular LLM fact-checkers in claim, sentence, and document levels. |
Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments (2023.acl-long)
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| Challenge: | Existing evaluations of human-in-the-loop systems to combat misinformation are often set up automatically using datasets that were retrospectively constructed. |
| Approach: | They propose a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that support them. |
| Outcome: | The proposed framework is based on modern NLP methods for human-in-the-loop fact-checking in the domain of COVID-19 treatments. |
Navigating the Political Compass: Evaluating Multilingual LLMs across Languages and Nationalities (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are ubiquitous in today’s technological landscape, boasting a plethora of applications, and even endangering human jobs in complex and creative fields. |
| Approach: | They evaluate the political bias of 15 multilingual LLMs using the Political Compass Test and assign a nationality to each model. |
| Outcome: | The models on the 50 most populous countries and their official languages exhibit political bias. |
Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence (2023.findings-acl)
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| Challenge: | Existing fact-checking benchmarks require systems to verify claims from everyday text against evidence from scientific journal articles. |
| Approach: | They propose a benchmark system that checks claims from news against scientific journal articles and veracity labels. |
| Outcome: | The new benchmark achieves F1 scores of 76.99 and 69.90 on both a fact-checking specific system and GPT-3.5, respectively. |
PolBiX: Detecting LLMs’ Political Bias in Fact-Checking through X-phemisms (2025.findings-emnlp)
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| Challenge: | a few models show tendencies of political bias, but this is not mitigated by explicitly calling for objectivism in prompts. |
| Approach: | They investigate political bias by exchanging words with euphemisms or dysphemismas in German claims. |
| Outcome: | The proposed model shows that political bias influences truthfulness assessment more than political leaning . |
Automated Fact-Checking in Dialogue: Are Specialized Models Needed? (2023.emnlp-main)
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| Challenge: | Prior work has shown that typical fact-checking models struggle with claims made in conversation. |
| Approach: | They propose to fine-tune models for dialogue on conversational data to improve performance on typical fact-checking. |
| Outcome: | The proposed models perform better on stand-alone claims than state-of-the-art models for dialogue while maintaining their performance on standalone claim. |
PACAR: Automated Fact-Checking with Planning and Customized Action Reasoning Using Large Language Models (2024.lrec-main)
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| Challenge: | Existing studies rely on idealized "gold" evidence for predictions, which is unrealistic due to its limited availability in real-world scenarios. |
| Approach: | They propose a fact-checking framework based on planning and customized action reasoning using LLMs. |
| Outcome: | The proposed framework outperforms baseline methods across three datasets and with varying complexity levels. |
STRICTA: Structured Reasoning in Critical Text Assessment for Peer Review and Beyond (2025.acl-long)
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| Challenge: | Existing work treats critical text assessment as a black box problem, limiting interpretability and human-AI collaboration. |
| Approach: | They propose a framework to model critical text assessment as an explicit, step-wise reasoning process. |
| Outcome: | The proposed framework breaks down assessment into a graph of interconnected reasoning steps drawing on causality theory. |
Adversarial Attacks Against Automated Fact-Checking: A Survey (2025.emnlp-main)
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Fanzhen Liu, Sharif Abuadbba, Kristen Moore, Surya Nepal, Cecile Paris, Jia Wu, Jian Yang, Quan Z. Sheng
| Challenge: | Existing fact-checking systems are vulnerable to adversarial attacks that manipulate or generate claims, evidence, or claim-evidence pairs. |
| Approach: | They examine the impact of adversarial attacks on existing AFC systems and examine their impact on existing ones. |
| Outcome: | The findings highlight the need for resilient fact-checking frameworks in limiting misinformation spread and supporting public trust. |
ClimateViz: A Benchmark for Statistical Reasoning and Fact Verification on Scientific Charts (2025.emnlp-main)
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| Challenge: | Scientific fact-checking has largely focused on textual and tabular sources, neglecting scientific charts. |
| Approach: | They propose a benchmark for scientific fact-checking grounded in scientific charts . climateViz comprises 49,862 claims paired with 2,896 visualizations . results show current models struggle to perform fact- checking when statistical reasoning is required . |
| Outcome: | The climateviz benchmark is the first large-scale benchmark for scientific fact-checking . it includes 49,862 claims paired with 2,896 visualizations labeled as support, refute, or not enough . |
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict (2024.lrec-main)
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| Challenge: | Existing methods to verify factuality of claims do not provide sufficient evidence for explainable fact-checking systems. |
| Approach: | They propose a method to automatically retrieve and summarize evidence from the Web and a novel multilingual explainable fact-checking dataset on the Russia-Ukraine conflict in 2022. |
| Outcome: | The proposed method can retrieve and summarize evidence from the Web and generate explanations in 16 languages. |
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. |
Refining Attention for Explainable and Noise-Robust Fact-Checking with Transformers (2025.emnlp-main)
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| Challenge: | Conventional transformer-based models falter due to noise sensitivity and lack explainability . ATTUN is a transformer architecture designed to enhance model transparency and resilience to noise. |
| Approach: | They propose a transformer architecture that enhances model transparency and resilience to noise . ATTUN is a module that directly modifies attention weights . they validated their approach using fact-checking datasets based on their results . |
| Outcome: | The proposed model improves predictions and identify relevant sections of input data. |
Triple-R: Automatic Reasoning for Fact Verification Using Language Models (2024.lrec-main)
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| Challenge: | Existing methods for fact-checking lack external sources and human-understandable explanations for decision-making . existing methods lack external knowledge sources and explanations . |
| Approach: | They propose a framework that uses the Web as an external knowledge source to retrieve relevant evidence for claims and generates reasons based on the retrieved evidence for datasets lacking explanations. |
| Outcome: | The proposed method improves the transparency and interpretability of fact-checking systems by providing human-understandable explanations for decision-making processes. |
A Causal Lens for Evaluating Faithfulness Metrics (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) offer natural language explanations as an alternative to feature attribution methods for model interpretability, but they may not reflect the model’s truereasoning faithfully. |
| Approach: | They propose a testbed framework for evaluating faithfulness metrics for natural language explanations using diagnosticity and model-editing methods. |
| Outcome: | The proposed framework evaluates faithfulness metrics for natural language explanations on four tasks including fact-checking, analogy, object counting, and multi-hop reasoning. |
HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims (2025.acl-long)
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| Challenge: | Identifying checkworthy claims is the first step, but detection methods struggle with content that is (1) multimodal, (2) from diverse domains, and (3) synthetic. |
| Approach: | They propose a dataset for multimodal checkworthiness detection with 27K real-world and synthetic image/claim pairs. |
| Outcome: | The proposed dataset compares lightweight text-based encoders to multimodal models but only focus on claim-like content. |
TSVer: A Benchmark for Fact Verification Against Time-Series Evidence (2025.emnlp-main)
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| Challenge: | Existing systems for fact-checking lack structured evidence, provide insufficient justifications for verdicts, or rely on synthetic claims. |
| Approach: | They propose a temporal and numerical reasoning dataset based on time-series evidence that is annotated with time frames and a verdict and justifications reflecting how the evidence is used to reach the verdict. |
| Outcome: | The proposed dataset improves the quality of the annotations and achieves an inter-annotator agreement of = 0.745 on verdicts. |
Explaining Sources of Uncertainty in Automated Fact-Checking (2026.acl-long)
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| Challenge: | Existing methods to explain model uncertainty as numbers or hedges do not reveal which evidence conflicts cause the uncertainty, leaving users unable to resolve disagreements. |
| Approach: | They propose a plug-and-play framework that generates natural-language explanations of model uncertainty grounded in conflicting/agreeing evidence. |
| Outcome: | The proposed framework generates explanations that more faithfully track model uncertainty and better align with the model’s fact-checking decisions than span-agnostic explanation prompting. |