Summary of Iclerb: In-context Learning Embedding and Reranker Benchmark, by Marie Al Ghossein and Emile Contal and Alexandre Robicquet
ICLERB: In-Context Learning Embedding and Reranker Benchmark
by Marie Al Ghossein, Emile Contal, Alexandre Robicquet
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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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 paper reframes Retrieval-Augmented Generation (RAG) in In-Context Learning (ICL) as a recommendation problem, aiming to select documents that maximize utility. This is done by introducing the In-Context Learning Embedding and Reranker Benchmark (ICLERB), a novel evaluation framework that compares retrievers based on their ability to enhance Large Language Model (LLM) accuracy in ICL settings. The paper also proposes a Reinforcement Learning-to-Rank from AI Feedback (RLRAIF) algorithm, designed to fine-tune retrieval models using minimal feedback from the LLM. Experimental results reveal notable differences between ICLERB and existing benchmarks, demonstrating that small models fine-tuned with RLRAIF outperform large state-of-the-art retrieval models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps Large Language Models learn new tasks by conditioning on prompts with relevant information. It does this by changing how we retrieve documents to help the model learn. The researchers created a special way to test and compare these document-retrieving methods, called the In-Context Learning Embedding and Reranker Benchmark (ICLERB). They also developed a new method to fine-tune these retrieval models using minimal feedback from the LLM. The results show that this new approach can help smaller models perform better than larger ones. |
Keywords
» Artificial intelligence » Embedding » Large language model » Rag » Reinforcement learning » Retrieval augmented generation
