  When using large language models (LLMs) in knowledge-intensive tasks, such as
open-domain question answering, external context can bridge a gap between
external knowledge and LLM's parametric knowledge. Recent research has been
developed to amplify contextual knowledge over the parametric knowledge of LLM
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.
