Summary of Assistrag: Boosting the Potential Of Large Language Models with An Intelligent Information Assistant, by Yujia Zhou et al.
AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistant
by Yujia Zhou, Zheng Liu, Zhicheng Dou
First submitted to arxiv on: 11 Nov 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 The proposed Assistant-based Retrieval-Augmented Generation (AssistRAG) framework addresses the limitations of Large Language Models (LLMs) in generating factually correct information. By integrating an intelligent information assistant within LLMs, AssistRAG improves information retrieval and decision-making capabilities. The two-phase training approach, Curriculum Assistant Learning and Reinforced Preference Optimization, enhances the performance of less advanced LLMs, outperforming benchmarks in complex reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) have made significant advances in natural language processing, but they often produce incorrect information. To fix this, a new way to combine these models with an assistant was created. This assistant helps the model find and use correct information better. The method is called AssistRAG. It uses two steps to train the model: first, it learns what’s important, then it optimizes its preferences. This makes less advanced LLMs work better and produces more accurate responses. |
Keywords
» Artificial intelligence » Natural language processing » Optimization » Retrieval augmented generation
