Summary of Retrollm: Empowering Large Language Models to Retrieve Fine-grained Evidence Within Generation, by Xiaoxi Li et al.
RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation
by Xiaoxi Li, Jiajie Jin, Yujia Zhou, Yongkang Wu, Zhonghua Li, Qi Ye, Zhicheng Dou
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary RetroLLM, a unified framework for large language models (LLMs), addresses limitations in retrieval-augmented generation (RAG) by integrating retrieval and generation into a single process. This enables LLMs to directly generate fine-grained evidence from the corpus with constrained decoding. The proposed approach introduces hierarchical FM-Index constraints and a forward-looking constrained decoding strategy to mitigate false pruning and 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RetroLLM is a new way for big language models to work better. It helps them generate more accurate answers by using information from the internet. This model is important because it makes language models less likely to make mistakes. It does this by making sure they only look at relevant information and don’t waste time looking at things that aren’t helpful. |
Keywords
» Artificial intelligence » Pruning » Rag » Retrieval augmented generation
