Summary of Mba-rag: a Bandit Approach For Adaptive Retrieval-augmented Generation Through Question Complexity, by Xiaqiang Tang et al.
MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question Complexity
by Xiaqiang Tang, Qiang Gao, Jian Li, Nan Du, Qi Li, Sihong Xie
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 Retrieval Augmented Generation (RAG) is a powerful technique that improves the performance of language models in tasks like knowledge-intensive question answering. However, current RAG frameworks are inefficient and suboptimal because they either perform retrieval indiscriminately or rely on rigid single-class classifiers to select retrieval methods. To address this, we propose a reinforcement learning-based framework that dynamically selects the most suitable retrieval strategy based on query complexity. Our solution leverages a multi-armed bandit algorithm to balance exploration and exploitation, and a dynamic reward function that balances accuracy and efficiency. We introduce penalties for methods that require more retrieval steps, even if they lead to correct results. Our method achieves new state-of-the-art results on multiple single-hop and multi-hop datasets while reducing retrieval costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to find information online, but it’s hard because there are too many places to look. That’s what happens when we try to use language models to answer questions that require a lot of knowledge. To fix this problem, we created a new way for computers to decide which sources to search based on how complex the question is. Our method uses a special algorithm to balance finding the right information with using too many resources. It even gives penalties to methods that take too long, even if they find the correct answer. Our method works better than others and can be used for multiple types of questions. |
Keywords
» Artificial intelligence » Question answering » Rag » Reinforcement learning » Retrieval augmented generation
