The Missing Parts: Augmenting Fact Verification with Half-Truth Detection

• URL: http://arxiv.org/abs/2508.00489v1
• Date: Fri, 01 Aug 2025 10:06:38 GMT
• Title: The Missing Parts: Augmenting Fact Verification with Half-Truth Detection
• Authors: Yixuan Tang, Jincheng Wang, Anthony K. H. Tung,
• Abstract summary: Many real-world claims are half-truths, factually correct yet misleading due to the omission of critical context.<n>We introduce the task of half-truth detection, and propose PolitiFact-Hidden, a new benchmark with 15k political claims annotated with sentence-level evidence alignment and inferred claim intent.<n>We present TRACER, a modular re-assessment framework that identifies omission-based misinformation by aligning evidence, inferring implied intent, and estimating the causal impact of hidden content.
• Score: 8.080157788477347
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Fact verification systems typically assess whether a claim is supported by retrieved evidence, assuming that truthfulness depends solely on what is stated. However, many real-world claims are half-truths, factually correct yet misleading due to the omission of critical context. Existing models struggle with such cases, as they are not designed to reason about what is left unsaid. We introduce the task of half-truth detection, and propose PolitiFact-Hidden, a new benchmark with 15k political claims annotated with sentence-level evidence alignment and inferred claim intent. To address this challenge, we present TRACER, a modular re-assessment framework that identifies omission-based misinformation by aligning evidence, inferring implied intent, and estimating the causal impact of hidden content. TRACER can be integrated into existing fact-checking pipelines and consistently improves performance across multiple strong baselines. Notably, it boosts Half-True classification F1 by up to 16 points, highlighting the importance of modeling omissions for trustworthy fact verification.

Related papers

• Is the Top Still Spinning? Evaluating Subjectivity in Narrative Understanding [50.94206345567363]
  Forcing binary labels upon ambiguous claims lowers the reliability of evaluation.<n>We introduce LLM-generated edits of summaries as a method of providing a nuanced evaluation of claims.<n>We show that ARM produces a absolute 21% improvement in annotator agreement on claim faithfulness.
  arXiv  Detail & Related papers  (2025-04-01T19:08:24Z)
• FactLens: Benchmarking Fine-Grained Fact Verification [6.814173254027381]
  We advocate for a shift towards fine-grained verification, where complex claims are broken down into smaller sub-claims for individual verification.<n>We introduce FactLens, a benchmark for evaluating fine-grained fact verification, with metrics and automated evaluators of sub-claim quality.<n>Our results show alignment between automated FactLens evaluators and human judgments, and we discuss the impact of sub-claim characteristics on the overall verification performance.
  arXiv  Detail & Related papers  (2024-11-08T21:26:57Z)
• Contrastive Learning to Improve Retrieval for Real-world Fact Checking [84.57583869042791]
  We present Contrastive Fact-Checking Reranker (CFR), an improved retriever for fact-checking complex claims. We leverage the AVeriTeC dataset, which annotates subquestions for claims with human written answers from evidence documents. We find a 6% improvement in veracity classification accuracy on the dataset.
  arXiv  Detail & Related papers  (2024-10-07T00:09:50Z)
• Robust Claim Verification Through Fact Detection [17.29665711917281]
  Our novel approach, FactDetect, leverages Large Language Models (LLMs) to generate concise factual statements from evidence. The generated facts are then combined with the claim and evidence. Our method demonstrates competitive results in the supervised claim verification model by 15% on the F1 score.
  arXiv  Detail & Related papers  (2024-07-25T20:03:43Z)
• Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence [59.81749318292707]
  We propose a fact verification model named ReRead to retrieve evidence and verify claim. The proposed system is able to achieve significant improvements upon best-reported models under different settings.
  arXiv  Detail & Related papers  (2023-05-02T03:23:14Z)
• Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation [67.69725605939315]
  Misinformation emerges in times of uncertainty when credible information is limited. This is challenging for NLP-based fact-checking as it relies on counter-evidence, which may not yet be available.
  arXiv  Detail & Related papers  (2022-10-25T09:40:48Z)
• Generating Literal and Implied Subquestions to Fact-check Complex Claims [64.81832149826035]
  We focus on decomposing a complex claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim. We present ClaimDecomp, a dataset of decompositions for over 1000 claims. We show that these subquestions can help identify relevant evidence to fact-check the full claim and derive the veracity through their answers.
  arXiv  Detail & Related papers  (2022-05-14T00:40:57Z)
• AmbiFC: Fact-Checking Ambiguous Claims with Evidence [57.7091560922174]
  We present AmbiFC, a fact-checking dataset with 10k claims derived from real-world information needs. We analyze disagreements arising from ambiguity when comparing claims against evidence in AmbiFC. We develop models for predicting veracity handling this ambiguity via soft labels.
  arXiv  Detail & Related papers  (2021-04-01T17:40:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.