Summary of Adapting to Non-stationary Environments: Multi-armed Bandit Enhanced Retrieval-augmented Generation on Knowledge Graphs, by Xiaqiang Tang et al.
Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphs
by Xiaqiang Tang, Jian Li, Nan Du, Sihong Xie
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 Multi-objective Multi-Armed Bandit enhanced RAG framework combines the Retrieval-Augmented Generation (RAG) framework with Knowledge Graphs to enhance the reasoning capabilities of Large language models. The system utilizes real-time user feedback to adapt to dynamic environments, selecting the most suitable retrieval method based on input queries and historical multi-objective performance. Experimental results demonstrate significant improvements in non-stationary settings and state-of-the-art performance in stationary environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to make Large language models better at understanding facts from extensive knowledge. They used a special framework called Retrieval-Augmented Generation (RAG) that combines with large databases of facts. The goal is to help the model reason more effectively, but they also want it to be good in changing environments and fast enough for people to use it. To achieve this, they created a new system that picks the best way to get information based on what users ask and how well each method has performed so far. They tested their approach with two sets of questions and found that it outperformed other methods in situations where things change quickly. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
