Summary of At-rag: An Adaptive Rag Model Enhancing Query Efficiency with Topic Filtering and Iterative Reasoning, by Mohammad Reza Rezaei et al.
AT-RAG: An Adaptive RAG Model Enhancing Query Efficiency with Topic Filtering and Iterative Reasoning
by Mohammad Reza Rezaei, Maziar Hafezi, Amit Satpathy, Lovell Hodge, Ebrahim Pourjafari
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 Medium Difficulty summary: Recent advancements in Question Answering (QA) with Large Language Models (LLMs) like GPT-4 have shown limitations in handling complex multi-hop queries. To address this challenge, we propose AT-RAG, a novel multistep Reasoning-based Attention Graph (RAG) incorporating topic modeling for efficient document retrieval and reasoning. Our model uses BERTopic to dynamically assign topics to queries, improving retrieval accuracy and efficiency. We evaluated AT-RAG on multihop benchmark datasets QA and a medical case study QA, achieving significant improvements in correctness, completeness, and relevance compared to existing methods. The integration of topic filtering and iterative reasoning enables our model to handle intricate queries efficiently, making it suitable for general tasks QA and complex domain-specific challenges like medical QA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about a new way to answer questions that are hard because they require looking at multiple sources. We’re proposing a new tool called AT-RAG that can help find the right information quickly and accurately. Our tool uses special techniques to figure out what topics or themes are relevant to a question, which helps it find better answers. We tested our tool on some challenging questions and found that it did much better than other tools at finding accurate and complete answers. This tool could be useful for many different applications where you need to quickly and accurately find the right information. |
Keywords
» Artificial intelligence » Attention » Gpt » Question answering » Rag
