Summary of Unims-rag: a Unified Multi-source Retrieval-augmented Generation For Personalized Dialogue Systems, by Hongru Wang et al.
UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems
by Hongru Wang, Wenyu Huang, Yang Deng, Rui Wang, Zezhong Wang, Yufei Wang, Fei Mi, Jeff Z. Pan, Kam-Fai Wong
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Large Language Models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation tasks, but personalization remains a significant challenge, particularly when dealing with multiple sources involved in dialogue systems. To address this issue, we decompose the problem into three sub-tasks: Knowledge Source Selection, Knowledge Retrieval, and Response Generation. Our novel Unified Multi-Source Retrieval-Augmented Generation system (UniMS-RAG) unifies these tasks within a sequence-to-sequence paradigm during training, enabling adaptive retrieval of evidences using acting tokens and evaluation tokens. This allows language models to interact with various knowledge sources and adapt their behavior to diverse task requirements. We also propose a self-refinement mechanism that iteratively refines the generated response considering consistency scores between the response and retrieved evidence, as well as relevance scores. Our experiments on two personalized datasets (DuLeMon and KBP) show that UniMS-RAG achieves state-of-the-art performance in knowledge source selection and response generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a conversation with an AI that can understand you perfectly and respond accordingly. This is the goal of a new kind of language model called Large Language Models (LLMs). However, when there are multiple sources involved in the conversation, it gets harder to make sure the AI understands what’s being said. To solve this problem, we broke down the task into three smaller steps: finding the right source, getting information from that source, and generating a response based on that information. We created a new system called UniMS-RAG that can do all these tasks at once. This allows the AI to adapt to different situations and make sure its responses are relevant and helpful. |
Keywords
» Artificial intelligence » Language model » Language understanding » Rag » Retrieval augmented generation
