FACTS&EVIDENCE: An Interactive Tool for Transparent Fine-Grained Factual Verification of Machine-Generated Text
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Abstract (arXiv)
With the widespread consumption of AI-generated content, there has been an increased focus on developing automated tools to verify the factual accuracy of such content. However, prior research and tools developed for fact verification treat it as a binary classification or a linear regression problem. Although this is a useful mechanism as part of automatic guardrails in systems, we argue that such tools lack transparency in the prediction reasoning and diversity in source evidence to provide a trustworthy user experience. We develop Facts&Evidence - an interactive and transparent tool for user-driven verification of complex text. The tool facilitates the intricate decision-making involved in fact-verification, presenting its users a breakdown of complex input texts to visualize the credibility of individual claims along with an explanation of model decisions and attribution to multiple, diverse evidence sources. Facts&Evidence aims to empower consumers of machine-generated text and give them agency to understand, verify, selectively trust and use such text.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 19 of its 34 references that are in the map. First posted on arXiv 19 Mar 2025.
Builds on
19 of 34 references.
References · 19
- 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
- 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
- 2021A Survey on Automated Fact-Checkingcited by 378
- 2021Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metricscited by 287
- 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 287
- 2024Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaborationcited by 180
- 2019MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claimscited by 169
- 2024Fine-grained Hallucination Detection and Editing for Language Modelscited by 149
- 2021Automated Fact-Checking for Assisting Human Fact-Checkerscited by 144
- 2022FaithDial: A Faithful Benchmark for Information-Seeking Dialoguecited by 109
- 2023Enhancing Uncertainty-Based Hallucination Detection with Stronger Focuscited by 99
- 2018Automated Fact Checking: Task formulations, methods and future directionscited by 96
7 more
- 2022Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Surveycited by 80
- 2023Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Modelscited by 67
- 2021DialFact: A Benchmark for Fact-Checking in Dialoguecited by 62
- 2022Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformationcited by 45
- 2020A Review on Fact Extraction and Verificationcited by 29
- 2024GenAudit: Fixing Factual Errors in Language Model Outputs with Evidencecited by 10
- 2024Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributionscited by 9
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.
Similar, not cited
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- 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking
- 2025Althea: The Fact-Checking--Metalearning Tradeoff in AI-Assisted Verification
- 2026PA-User: Simulating Trust and Verification under AI-Generated Content ↗
- 2023The Perils & Promises of Fact-checking with Large Language Models
- 2024AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators
- 2024Evidence-Based Temporal Fact Verification ↗
- 2025Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs
- 2024Multimodal Large Language Models to Support Real-World Fact-Checking