CrEst: Credibility Estimation for Contexts in LLMs via Weak Supervision

• URL: http://arxiv.org/abs/2506.14912v1
• Date: Tue, 17 Jun 2025 18:44:21 GMT
• Title: CrEst: Credibility Estimation for Contexts in LLMs via Weak Supervision
• Authors: Dyah Adila, Shuai Zhang, Boran Han, Bonan Min, Yuyang Wang,
• Abstract summary: CrEst is a weakly supervised framework for assessing the credibility of context documents during inference.<n>Experiments across three model architectures and five datasets demonstrate that CrEst consistently outperforms strong baselines.
• Score: 15.604947362541415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of contextual information has significantly enhanced the performance of large language models (LLMs) on knowledge-intensive tasks. However, existing methods often overlook a critical challenge: the credibility of context documents can vary widely, potentially leading to the propagation of unreliable information. In this paper, we introduce CrEst, a novel weakly supervised framework for assessing the credibility of context documents during LLM inference--without requiring manual annotations. Our approach is grounded in the insight that credible documents tend to exhibit higher semantic coherence with other credible documents, enabling automated credibility estimation through inter-document agreement. To incorporate credibility into LLM inference, we propose two integration strategies: a black-box approach for models without access to internal weights or activations, and a white-box method that directly modifies attention mechanisms. Extensive experiments across three model architectures and five datasets demonstrate that CrEst consistently outperforms strong baselines, achieving up to a 26.86% improvement in accuracy and a 3.49% increase in F1 score. Further analysis shows that CrEst maintains robust performance even under high-noise conditions.

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