AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistant

• URL: http://arxiv.org/abs/2411.06805v1
• Date: Mon, 11 Nov 2024 09:03:52 GMT
• Title: AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistant
• Authors: Yujia Zhou, Zheng Liu, Zhicheng Dou,
• Abstract summary: Large Language Models generate factually incorrect information, known as "hallucination" To cope with these challenges, we propose Assistant-based Retrieval-Augmented Generation (AssistRAG) This assistant manages memory and knowledge through tool usage, action execution, memory building, and plan specification.
• Score: 23.366991558162695
• License:
• Abstract: The emergence of Large Language Models (LLMs) has significantly advanced natural language processing, but these models often generate factually incorrect information, known as "hallucination". Initial retrieval-augmented generation (RAG) methods like the "Retrieve-Read" framework was inadequate for complex reasoning tasks. Subsequent prompt-based RAG strategies and Supervised Fine-Tuning (SFT) methods improved performance but required frequent retraining and risked altering foundational LLM capabilities. To cope with these challenges, we propose Assistant-based Retrieval-Augmented Generation (AssistRAG), integrating an intelligent information assistant within LLMs. This assistant manages memory and knowledge through tool usage, action execution, memory building, and plan specification. Using a two-phase training approach, Curriculum Assistant Learning and Reinforced Preference Optimization. AssistRAG enhances information retrieval and decision-making. Experiments show AssistRAG significantly outperforms benchmarks, especially benefiting less advanced LLMs, by providing superior reasoning capabilities and accurate responses.

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