Augmenting Large Language Models (LLMs) with retrieved external knowledge has proven effective for improving the factual accuracy of generated responses. Despite their success, retrieval-augmented LLMs still face the distractibility issue, where the generated responses are negatively influenced by noise from both external and internal knowledge sources. In this paper, we introduce a novel, training-free decoding method guided by entropy considerations to mitigate this issue. Our approach utilizes entropy-based document-parallel ensemble decoding to prioritize low-entropy distributions from retrieved documents, thereby enhancing the extraction of relevant information of context. Additionally, it incorporates a contrastive decoding mechanism that contrasts the obtained low-entropy ensemble distribution with the high-entropy distribution derived from the model's internal knowledge across layers, which ensures a greater emphasis on reliable external information. Extensive experiments on open-domain question answering datasets demonstrate the superiority of our method.
View on arXiv@article{qiu2025_2406.17519,
title={ Entropy-Based Decoding for Retrieval-Augmented Large Language Models },
author={ Zexuan Qiu and Zijing Ou and Bin Wu and Jingjing Li and Aiwei Liu and Irwin King },
journal={arXiv preprint arXiv:2406.17519},
year={ 2025 }
}