FACTS&EVIDENCE: An Interactive Tool for Transparent Fine-Grained Factual Verification of Machine-Generated Text

2025-03-19

AI Summary

Existing AI-generated content (AIGC) fact-checking tools predominantly rely on black-box binary classification or regression models, suffering from poor interpretability, limited evidence diversity, and minimal user interactivity. Method: We propose the first user-driven, fine-grained fact verification framework that decomposes long texts into atomic claims, integrates heterogeneous multi-source evidence (e.g., knowledge bases, web pages, documents), and employs cross-source evidence fusion with an interpretable reasoning model to produce claim-level confidence scores and natural-language explanations—supporting multi-hop provenance tracing and dynamic user feedback. Contribution/Results: Our framework breaks from conventional paradigms by enabling transparent, traceable, evidence-diverse, and human-AI collaborative verification. Experiments demonstrate significant improvements in user verification efficiency (+37%) and trust (+42%), establishing a novel paradigm for trustworthy AIGC interaction.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

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.

Problem

• Develops tool for transparent factual verification of AI-generated text
• Addresses lack of transparency in existing fact verification tools
• Enables user-driven verification with diverse evidence sources

Innovation

• Interactive tool for transparent factual verification
• Visualizes credibility breakdown of individual claims
• Attributes decisions to diverse evidence sources