Summary of Context Awareness Gate For Retrieval Augmented Generation, by Mohammad Hassan Heydari et al.
Context Awareness Gate For Retrieval Augmented Generation
by Mohammad Hassan Heydari, Arshia Hemmat, Erfan Naman, Afsaneh Fatemi
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Context Awareness Gate (CAG) architecture is designed to improve the accuracy of large language models (LLMs) by dynamically adjusting their input prompts based on whether external context retrieval is necessary. This approach addresses the issue of retrieving irrelevant information, which can impair the quality of LLM outputs in open-domain question answering tasks. By leveraging a novel Vector Candidates method that is statistical, LLM-independent, and highly scalable, CAG aims to enhance the overall performance of Retrieval Augmented Generation (RAG) systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how large language models can be better at answering questions by using external information. Right now, these models often get confused by irrelevant information they find online. The authors suggest a new way to make these models work better by adjusting what they look for based on the question being asked. They also introduce a special tool called Vector Candidates that helps the model focus on the right information. |
Keywords
» Artificial intelligence » Question answering » Rag » Retrieval augmented generation
