TL;DR
TRACE introduces a framework that provides nuanced web content reliability scores along with contextual explanations, enhancing transparency in misinformation detection.
Contribution
It presents a novel large-scale dataset with continuous reliability annotations and a fine-tuned model outperforming baselines in credibility assessment.
Findings
TrueGL-1B outperforms baseline models on regression metrics
The dataset includes 140,000 articles with 35 reliability scores
Code and model are publicly available for future research
Abstract
In an era of AI-generated misinformation flooding the web, existing tools struggle to empower users with nuanced, transparent assessments of content credibility. They often default to binary (true/false) classifications without contextual justifications, leaving users vulnerable to disinformation. We address this gap by introducing TRACE: Transparent Reliability Assessment with Contextual Explanations, a unified framework that performs two key tasks: (1) it assigns a fine-grained, continuous reliability score (from 0.1 to 1.0) to web content, and (2) it generates a contextual explanation for its assessment. The core of TRACE is the TrueGL-1B model, fine-tuned on a novel, large-scale dataset of over 140,000 articles. This dataset's primary contribution is its annotation with 35 distinct continuous reliability scores, created using a Human-LLM co-creation and data poisoning paradigm. This…
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Code & Models
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