Summary of Better Rag Using Relevant Information Gain, by Marc Pickett et al.
Better RAG using Relevant Information Gain
by Marc Pickett, Jeremy Hartman, Ayan Kumar Bhowmick, Raquib-ul Alam, Aditya Vempaty
First submitted to arxiv on: 16 Jul 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 This paper proposes a novel optimization metric based on relevant information gain to enhance retrieval augmented generation (RAG) in large language models. By optimizing this metric, which measures the total information relevant to a query for a set of retrieved results, diversity among retrieved passages emerges organically. This approach outperforms existing methods that directly optimize for relevance and diversity on question answering tasks from the Retrieval Augmented Generation Benchmark (RGB). The authors suggest that previous methods, such as Maximal Marginal Relevance (MMR), can limit the number of retrieved passages that inform a model’s response due to context window limitations. To address this, they introduce an optimization metric that balances diversity and relevance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making large language models better at answering questions by finding the most relevant information from a big database. They do this by using a new way of calculating how good the answers are, which helps to make sure that different pieces of information are used. This makes the model more diverse and can answer harder questions correctly. |
Keywords
» Artificial intelligence » Context window » Optimization » Question answering » Rag » Retrieval augmented generation
