Challenge: generative AI is a counter-measure to misinformation, but factual claim detection suffers from inconsistency in definitions and high cost of manual annotation.
Approach: They propose a framework that assists in the annotation of factual claims with the help of large language models.
Outcome: The proposed framework can be used to annotate factual claims with the help of large language models and can work with or without expert supervision.

Similar Papers

Towards Effective Extraction and Evaluation of Factual Claims (2025.acl-long)

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Challenge: Lack of a standardized evaluation framework impedes assessment and comparison of claim extraction methods.
Approach: They propose a framework for evaluating claim extraction in the context of fact-checking . they also introduce Claimify, an LLM-based claim extraction method .
Outcome: The proposed evaluation framework outperforms existing methods in the evaluation of claim extraction methods.
A Thesis Proposal ClaimInspector Framework: A Hybrid Approach to Data Annotation using Fact-Checked Claims and LLMs (2024.eacl-srw)

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Challenge: Current efforts focus on textual claims sourced mainly from Twitter . lack of automated control measures and reliance on human annotation increase noise risk .
Approach: They propose to use a framework to integrate data annotation to mitigate misinformation . they propose to include fact-checks alongside the corresponding claims made by politicians .
Outcome: The proposed dataset will include fact-checks alongside the corresponding claims made by politicians.
Claim Check-Worthiness Detection as Positive Unlabelled Learning (2020.findings-emnlp)

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Challenge: a unified approach to claim check-worthiness detection is a critical component of fact checking systems.
Approach: They propose a unified approach which corrects for misinformation by positive unlabelled learning . they propose citation needed detection from Wikipedia and a ranking task which is a critical component of automatic fact checking systems.
Outcome: The proposed method outperforms the state of the art in two of the three tasks studied in English.
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers (2024.findings-emnlp)

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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.
FactAppeal: Identifying Epistemic Factual Appeals in News Media (2026.findings-eacl)

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Challenge: Existing methods focus on the content of factual statements and ignore the epistemic structures that confer credibility and persuasive force to these claims.
Approach: They propose a task of Epistemic Appeal Identification to identify whether and how factual statements have been anchored by external sources or evidence.
Outcome: The proposed task identifies whether and how factual statements have been anchored by external sources or evidence.
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.
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.
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.
Outcome: The proposed frameworks are based on large-scale models and new methods such as Retrieval Augmented Generation (RAG).
FactLens: Benchmarking Fine-Grained Fact Verification (2025.findings-acl)

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Challenge: Large Language Models (LLMs) have shown impressive capability in language generation and understanding, but their tendency to hallucinate and produce factually incorrect information remains a key limitation.
Approach: They propose a benchmark to evaluate fine-grained fact verification where claims are broken down into smaller sub-claims for individual verification.
Outcome: The proposed model enables more precise identification of inaccuracies, improved transparency, and reduced ambiguity in evidence retrieval.
ClaimLens: Automated, Explainable Fact-Checking on Voting Claims Using Frame-Semantics (2024.emnlp-demo)

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Challenge: Existing fact-checking solutions lack transparency and explainability . a lack of transparency can make it difficult for users to trust and understand the reasoning behind the outcomes.
Approach: They propose an automated fact-checking system focused on voting-related factual claims that leverages frame-semantic parsing to provide structured and interpretable fact verification.
Outcome: The proposed system can extract relevant information from voting-related factual claims using public records and Vote semantic frame.

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