  Retrieval-augmented generation (RAG) has shown promising potential to enhance
the accuracy and factuality of language models (LMs). However, imperfect
retrievers or noisy corpora can introduce misleading or even erroneous
information to the retrieved contents, posing a significant challenge to the
generation quality. Existing RAG methods typically address this challenge by
directly predicting final answers despite potentially noisy inputs, resulting
in an implicit denoising process that is difficult to interpret and verify. On
the other hand, the acquisition of explicit denoising supervision is often
costly, involving significant human efforts. In this work, we propose
InstructRAG, where LMs explicitly learn the denoising process through
self-synthesized rationales -- First, we instruct the LM to explain how the
ground-truth answer is derived from retrieved documents. Then, these rationales
can be used either as demonstrations for in-context learning of explicit
denoising or as supervised fine-tuning data to train the model. Compared to
standard RAG approaches, InstructRAG requires no additional supervision, allows
for easier verification of the predicted answers, and effectively improves
generation accuracy. Experiments show InstructRAG consistently outperforms
existing RAG methods in both training-free and trainable scenarios, achieving a
relative improvement of 8.3% over the best baseline method on average across
five knowledge-intensive benchmarks. Extensive analysis indicates that
InstructRAG scales well with increased numbers of retrieved documents and
consistently exhibits robust denoising ability even in out-of-domain datasets,
demonstrating strong generalizability.
