Entropy-Based Decoding for Retrieval-Augmented Large Language Models
<|reference_start|>Entropy-Based Decoding for Retrieval-Augmented Large Language Models: 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.<|reference_end|>
@article{qiu2024entropy-based,
    title={Entropy-Based Decoding for Retrieval-Augmented Large Language Models},
    author={Zexuan Qiu, Zijing Ou, Bin Wu, Jingjing Li, Aiwei Liu, Irwin King},
    journal={arXiv preprint arXiv:2406.17519},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17519},
    primaryClass={cs.CL}
}