Summary of Adaptive-rag: Learning to Adapt Retrieval-augmented Large Language Models Through Question Complexity, by Soyeong Jeong et al.
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
by Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, Jong C. Park
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 adaptive Question-Answering (QA) framework combines retrieval-augmented Large Language Models (LLMs) with a classifier to dynamically select the most suitable strategy for handling queries of varying complexity. The approach seamlessly adapts between iterative and single-step retrieval-augmented LLMs, as well as no-retrieval methods, based on query complexity. This results in enhanced efficiency and accuracy compared to relevant baselines, including adaptive retrieval approaches. The framework is validated on open-domain QA datasets covering multiple query complexities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way for computers to answer questions. It’s called the Adaptive Question-Answering (QA) framework. This framework helps computers figure out how to answer questions by choosing the best method based on the complexity of the question. The framework uses two main parts: a language model and a classifier. The language model is like a super smart computer that can understand what people are saying, and the classifier is like a special kind of judge that decides which method to use for each question. This approach helps computers answer questions more accurately and efficiently than before. |
Keywords
» Artificial intelligence » Language model » Question answering
