Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts
<|reference_start|>Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts: When using large language models (LLMs) in knowledge-intensive tasks, such as open-domain question answering, external context can bridge the gap between external knowledge and the LLMs' parametric knowledge. Recent research has been developed to amplify contextual knowledge over the parametric knowledge of LLMs with contrastive decoding approaches. While these approaches could yield truthful responses when relevant context is provided, they are prone to vulnerabilities when faced with noisy contexts. We extend the scope of previous studies to encompass noisy contexts and propose adaptive contrastive decoding (ACD) to leverage contextual influence effectively. ACD demonstrates improvements in open-domain question answering tasks compared to baselines, especially in robustness by remaining undistracted by noisy contexts in retrieval-augmented generation.<|reference_end|>
@article{kim2024adaptive,
    title={Adaptive Contrastive Decoding in Retrieval-Augmented Generation for
  Handling Noisy Contexts},
    author={Youna Kim, Hyuhng Joon Kim, Cheonbok Park, Choonghyun Park, Hyunsoo
  Cho, Junyeob Kim, Kang Min Yoo, Sang-goo Lee, Taeuk Kim},
    journal={arXiv preprint arXiv:2408.01084},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01084},
    primaryClass={cs.CL}
}