ICLERB: In-Context Learning Embedding and Reranker Benchmark

• URL: http://arxiv.org/abs/2411.18947v1
• Date: Thu, 28 Nov 2024 06:28:45 GMT
• Title: ICLERB: In-Context Learning Embedding and Reranker Benchmark
• Abstract summary: In-Context Learning (ICL) enables Large Language Models to perform new tasks by conditioning on prompts with relevant information.<n>Traditional retrieval methods focus on semantic relevance, treating retrieval as a search problem.<n>We propose reframing retrieval for ICL as a recommendation problem, aiming to select documents that maximize utility in ICL tasks.
• Score: 45.40331863265474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In-Context Learning (ICL) enables Large Language Models (LLMs) to perform new tasks by conditioning on prompts with relevant information. Retrieval-Augmented Generation (RAG) enhances ICL by incorporating retrieved documents into the LLM's context at query time. However, traditional retrieval methods focus on semantic relevance, treating retrieval as a search problem. In this paper, we propose reframing retrieval for ICL as a recommendation problem, aiming to select documents that maximize utility in ICL tasks. We introduce the In-Context Learning Embedding and Reranker Benchmark (ICLERB), a novel evaluation framework that compares retrievers based on their ability to enhance LLM accuracy in ICL settings. Additionally, we propose a novel Reinforcement Learning-to-Rank from AI Feedback (RLRAIF) algorithm, designed to fine-tune retrieval models using minimal feedback from the LLM. Our experimental results reveal notable differences between ICLERB and existing benchmarks, and demonstrate that small models fine-tuned with our RLRAIF algorithm outperform large state-of-the-art retrieval models. These findings highlight the limitations of existing evaluation methods and the need for specialized benchmarks and training strategies adapted to ICL.

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