Enhancing Retrieval-Augmented Generation: A Study of Best Practices

• URL: http://arxiv.org/abs/2501.07391v1
• Date: Mon, 13 Jan 2025 15:07:55 GMT
• Title: Enhancing Retrieval-Augmented Generation: A Study of Best Practices
• Authors: Siran Li, Linus Stenzel, Carsten Eickhoff, Seyed Ali Bahrainian,
• Abstract summary: We develop advanced RAG system designs that incorporate query expansion, various novel retrieval strategies, and a novel Contrastive In-Context Learning RAG. Our study systematically investigates key factors, including language model size, prompt design, document chunk size, knowledge base size, retrieval stride, query expansion techniques, and Focus Mode retrieving relevant context at sentence-level. Our findings offer actionable insights for developing RAG systems, striking a balance between contextual richness and retrieval-generation efficiency.
• Score: 16.246719783032436
• License:
• Abstract: Retrieval-Augmented Generation (RAG) systems have recently shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses. However, the influence of various components and configurations within RAG systems remains underexplored. A comprehensive understanding of these elements is essential for tailoring RAG systems to complex retrieval tasks and ensuring optimal performance across diverse applications. In this paper, we develop several advanced RAG system designs that incorporate query expansion, various novel retrieval strategies, and a novel Contrastive In-Context Learning RAG. Our study systematically investigates key factors, including language model size, prompt design, document chunk size, knowledge base size, retrieval stride, query expansion techniques, Contrastive In-Context Learning knowledge bases, multilingual knowledge bases, and Focus Mode retrieving relevant context at sentence-level. Through extensive experimentation, we provide a detailed analysis of how these factors influence response quality. Our findings offer actionable insights for developing RAG systems, striking a balance between contextual richness and retrieval-generation efficiency, thereby paving the way for more adaptable and high-performing RAG frameworks in diverse real-world scenarios. Our code and implementation details are publicly available.

Related papers

• A Survey of Query Optimization in Large Language Models [10.255235456427037]
  RAG mitigates the limitations of Large Language Models by dynamically retrieving and leveraging up-to-date relevant information. QO has emerged as a critical element, playing a pivotal role in determining the effectiveness of RAG's retrieval stage.
  arXiv  Detail & Related papers  (2024-12-23T13:26:04Z)
• Unanswerability Evaluation for Retrieval Augmented Generation [74.3022365715597]
  UAEval4RAG is a framework designed to evaluate whether RAG systems can handle unanswerable queries effectively. We define a taxonomy with six unanswerable categories, and UAEval4RAG automatically synthesizes diverse and challenging queries.
  arXiv  Detail & Related papers  (2024-12-16T19:11:55Z)
• CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation [68.81271028921647]
  We introduce CORAL, a benchmark designed to assess RAG systems in realistic multi-turn conversational settings. CORAL includes diverse information-seeking conversations automatically derived from Wikipedia. It supports three core tasks of conversational RAG: passage retrieval, response generation, and citation labeling.
  arXiv  Detail & Related papers  (2024-10-30T15:06:32Z)
• StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization [94.31508613367296]
  Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs) We propose StructRAG, which can identify the optimal structure type for the task at hand, reconstruct original documents into this structured format, and infer answers based on the resulting structure. Experiments show that StructRAG achieves state-of-the-art performance, particularly excelling in challenging scenarios.
  arXiv  Detail & Related papers  (2024-10-11T13:52:44Z)
• A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions [0.0]
  RAG combines retrieval mechanisms with generative language models to enhance the accuracy of outputs. Recent research breakthroughs are discussed, highlighting novel methods for improving retrieval efficiency. Future research directions are proposed, focusing on improving the robustness of RAG models.
  arXiv  Detail & Related papers  (2024-10-03T22:29:47Z)
• Searching for Best Practices in Retrieval-Augmented Generation [31.438681543849224]
  Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information. Here, we investigate existing RAG approaches and their potential combinations to identify optimal RAG practices. We suggest several strategies for deploying RAG that balance both performance and efficiency.
  arXiv  Detail & Related papers  (2024-07-01T12:06:34Z)
• Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning [49.3242278912771]
  We introduce a novel multimodal RAG framework named RMR (Retrieval Meets Reasoning) The RMR framework employs a bi-modal retrieval module to identify the most relevant question-answer pairs. It significantly boosts the performance of various vision-language models across a spectrum of benchmark datasets.
  arXiv  Detail & Related papers  (2024-05-31T14:23:49Z)
• RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems [51.171355532527365]
  Retrieval-augmented generation (RAG) can significantly improve the performance of language models (LMs) RAGGED is a framework for analyzing RAG configurations across various document-based question answering tasks.
  arXiv  Detail & Related papers  (2024-03-14T02:26:31Z)
• CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models [49.16989035566899]
  Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios.
  arXiv  Detail & Related papers  (2024-01-30T14:25:32Z)
• Retrieval-Augmented Generation for Large Language Models: A Survey [17.82361213043507]
  Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases.
  arXiv  Detail & Related papers  (2023-12-18T07:47:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.