RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation

Xiaoxi Li, Jiajie Jin, Yujia Zhou, YongkangWu, LiZhongHua, Qi Ye, Zhicheng Dou

RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation: 36 upvotes on Hugging Face Daily Papers, #3 of 28 papers on 2024-12-17. Day-by-day upvote history.

Large language models (LLMs) exhibit remarkable generative capabilities but often suffer from hallucinations. Retrieval-augmented generation (RAG) offers an effective solution by incorporating external knowledge, but existing methods still face several limitations: additional deployment costs of separate retrievers, redundant input tokens from retrieved text chunks, and the lack of joint optimization of retrieval and generation. To address these issues, we propose RetroLLM, a unified framework that integrates retrieval and generation into a single, cohesive process, enabling LLMs to directly generate fine-grained evidence from the corpus with constrained decoding. Moreover, to mitigate false pruning in the process of constrained evidence generation, we introduce (1) hierarchical FM-Index constraints, which generate corpus-constrained clues to identify a subset of relevant documents before evidence generation, reducing irrelevant decoding space; and (2) a forward-looking constrained decoding strategy, which considers the relevance of future sequences to improve evidence accuracy. Extensive experiments on five open-domain QA datasets demonstrate RetroLLM's superior performance across both in-domain and out-of-domain tasks. The code is available at https://github.com/sunnynexus/RetroLLM.

Paper page on Hugging Face · arXiv

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