Summary of Im-rag: Multi-round Retrieval-augmented Generation Through Learning Inner Monologues, by Diji Yang et al.
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues
by Diji Yang, Jinmeng Rao, Kezhen Chen, Xiaoyuan Guo, Yawen Zhang, Jie Yang, Yi Zhang
First submitted to arxiv on: 15 May 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 This paper proposes a novel Large Language Model (LLM)-centric approach, IM-RAG, to enhance the outputs of LLMs by integrating Information Retrieval (IR) systems. The approach addresses limitations in traditional Retrieval-Augmented Generation (RAG) paradigms, including limited flexibility and constrained interpretability during multi-round retrieval processes. IM-RAG integrates IR systems with LLMs, enabling learning Inner Monologues (IM) to support multi-round RAG. The Reasoner (LLM) proposes queries or provides final answers based on conversational context, while the Refiner refines outputs from the Retriever. Reinforcement Learning (RL) optimizes the IM process with a Progress Tracker providing mid-step rewards and Supervised Fine-Tuning (SFT) optimizing answer prediction. Experiments with the HotPotQA dataset demonstrate state-of-the-art performance and flexibility in integrating IR modules, as well as strong interpretability in learned inner monologues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make a computer program called a Large Language Model better by letting it talk to another part that can find information. Right now, these programs are limited because they don’t always get the right answer and can’t understand what’s happening inside their own “mind.” The new approach is called IM-RAG and it helps the program learn how to ask questions and get answers from different sources. This makes it better at understanding and giving accurate responses. The researchers tested this idea with a big dataset of questions and found that it works really well! |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Rag » Reinforcement learning » Retrieval augmented generation » Supervised
