OpenDecoder: Open Large Language Model Decoding to Incorporate Document Quality in RAG
This work proposes OpenDecoder, a novel framework that addresses the challenge of inconsistent retrieval quality in Retrieval-Augmented Generation (RAG) systems, which often undermines answer accuracy. OpenDecoder is the first approach to explicitly integrate multi-dimensional document quality signals—including relevance scores, ranking positions, and query performance prediction metrics—directly into the decoding process of large language models (LLMs), enabling quality-aware generation control. The framework is highly flexible, allowing seamless incorporation of arbitrary external quality indicators and compatibility with various LLM post-training objectives. Extensive experiments across five benchmark datasets demonstrate that OpenDecoder significantly outperforms existing baselines, substantially enhancing the robustness of RAG systems against noisy contexts and improving the reliability of generated responses.