OpenDecoder: Open Large Language Model Decoding to Incorporate Document Quality in RAG
The development of large language models (LLMs) has achieved superior performance in a range of downstream tasks, including LLM-based retrieval-augmented generation (RAG). The quality of generated content heavily relies on the usefulness of the retrieved information and the capacity of LLMs' internal information processing mechanism to incorporate it in answer generation. It is generally assumed that the retrieved information is relevant to the question. However, the retrieved information may have a variable degree of relevance and usefulness, depending on the question and the document collection. It is important to take into account the relevance of the retrieved information in answer generation. In this paper, we propose OpenDecoder, a new approach that leverages explicit evaluation of the retrieved information as quality indicator features for generation. We aim to build a RAG model that is more robust to varying levels of noisy context. Three types of explicit evaluation information are considered: relevance score, ranking score, and QPP (query performance prediction) score. The experimental results on five benchmark datasets demonstrate the effectiveness and better robustness of OpenDecoder by outperforming various baseline methods. Importantly, this paradigm is flexible to be integrated with the post-training of LLMs for any purposes and incorporated with any type of external indicators.
69686b880ac10a06522f6aae
Fengran Mo
claimed_verified
2026-01-16T10:32:41.127Z
6570cc04c4993b8fb975a2e3
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Fengran Mo
fengran
user
fengran
69686b880ac10a06522f6aaf
Zhan Su
69686b880ac10a06522f6ab0
Yuchen Hui
claimed_verified
2026-01-15T09:33:41.214Z
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Yuchen Hui
Meranti
user
Meranti
69686b880ac10a06522f6ab1
Jinghan Zhang
69686b880ac10a06522f6ab2
Jia Ao Sun
69686b880ac10a06522f6ab3
Zheyuan Liu
69686b880ac10a06522f6ab4
Chao Zhang
69686b880ac10a06522f6ab5
Tetsuya Sakai
69686b880ac10a06522f6ab6
Jian-Yun Nie
OpenDecoder: Open Large Language Model Decoding to Incorporate Document Quality in RAG
The development of large language models (LLMs) has achieved superior performance in a range of downstream tasks, including LLM-based retrieval-augmented generation (RAG). The quality of generated content heavily relies on the usefulness of the retrieved information and the capacity of LLMs' internal information processing mechanism to incorporate it in answer generation. It is generally assumed that the retrieved information is relevant to the question. However, the retrieved information may have a variable degree of relevance and usefulness, depending on the question and the document collection. It is important to take into account the relevance of the retrieved information in answer generation. In this paper, we propose OpenDecoder, a new approach that leverages explicit evaluation of the retrieved information as quality indicator features for generation. We aim to build a RAG model that is more robust to varying levels of noisy context. Three types of explicit evaluation information are considered: relevance score, ranking score, and QPP (query performance prediction) score. The experimental results on five benchmark datasets demonstrate the effectiveness and better robustness of OpenDecoder by outperforming various baseline methods. Importantly, this paradigm is flexible to be integrated with the post-training of LLMs for any purposes and incorporated with any type of external indicators.
OpenDecoder enhances retrieval-augmented generation by explicitly evaluating retrieved information quality through relevance, ranking, and query performance prediction scores, improving robustness to noisy context.
large language models retrieval-augmented generation QPP query performance prediction relevance score ranking score post-training