TRACE: Transparent Web Reliability Assessment with Contextual Explanations

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 method overcomes the limitations of binary-labeled datasets by populating the mid-ranges of reliability. In our evaluation, TrueGL-1B consistently outperforms other small-scale LLM baselines and rule-based approaches on key regression metrics, including MAE, RMSE, and R2. The model's high accuracy and interpretable justifications make trustworthy information more accessible. To foster future research, our code and model are made publicly available here: github.com/zade90/TrueGL.