Summary of Unsupervised Information Refinement Training Of Large Language Models For Retrieval-augmented Generation, by Shicheng Xu et al.
Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation
by Shicheng Xu, Liang Pang, Mo Yu, Fandong Meng, Huawei Shen, Xueqi Cheng, Jie Zhou
First submitted to arxiv on: 28 Feb 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 perspective on large language models (LLMs) in retrieval-augmented generation (RAG), where LLMs act as “Information Refiners” that consistently integrate knowledge within retrieved texts and model parameters to generate more concise, accurate, and complete texts. The authors introduce an information refinement training method called InFO-RAG that optimizes LLMs for RAG in an unsupervised manner, which is low-cost and general across various tasks. Extensive experiments on zero-shot prediction of 11 datasets show that InFO-RAG improves the performance of LLaMA2 by an average of 9.39% relative points, also demonstrating advantages in in-context learning and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how large language models can use information from other sources to generate better text. Right now, these models are not very good at using this extra information correctly. The authors suggest that instead of trying to get the correct information, the model should focus on integrating the knowledge it already has with the new information. They developed a special way of training the model to do this, called InFO-RAG. This method was tested on many different types of tasks and showed significant improvement over previous methods. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation » Unsupervised » Zero shot
