Summary of Stochastic Rag: End-to-end Retrieval-augmented Generation Through Expected Utility Maximization, by Hamed Zamani and Michael Bendersky
Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization
by Hamed Zamani, Michael Bendersky
First submitted to arxiv on: 5 May 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 This paper introduces Stochastic RAG, a novel approach for optimizing retrieval-augmented generation (RAG) models that relaxes previous assumptions. The proposed method casts the retrieval process as a stochastic sampling without replacement process, enabling end-to-end optimization using straight-through Gumbel-top-k. This is applied to a recent and effective RAG model, achieving state-of-the-art results on six out of seven datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Stochastic RAG is a new way to improve computer programs that generate text based on what’s already been written. It does this by changing how the program “looks” for relevant information, which helps it create more accurate and helpful responses. This method was tested on many different tasks and datasets, and showed great results in most cases. |
Keywords
» Artificial intelligence » Optimization » Rag » Retrieval augmented generation
