RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems

2024-03-14

arXiv.org

AI Summary

RAG system performance critically depends on the retriever-reader configuration, retrieval depth, and context quality; suboptimal settings often degrade performance rather than improve it. Method: We propose RAGGED, a systematic evaluation framework that—through multidimensional controlled experiments, controlled noise injection, and response attribution analysis—quantifies language models’ sensitivity spectra to contextual signals versus noise, and establishes a behavior-driven diagnostic paradigm for RAG configuration. Contribution/Results: We identify two canonical performance patterns—monotonic improvement and inverted-U—as context quality varies. Crucially, we reveal fundamental disparities across models in noise robustness and signal utilization capacity. Based on these insights, we distill reusable, model-aware configuration principles, validated across multiple DBQA benchmarks for both effectiveness and generalizability.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Search and Optimization: Algorithm Configuration
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Retrieval-augmented generation (RAG) can significantly improve the performance of language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). However, the effectiveness of RAG is highly dependent on its configuration. To systematically find the optimal configuration, we introduce RAGGED, a framework for analyzing RAG configurations across various DBQA tasks. Using the framework, we discover distinct LM behaviors in response to varying context quantities, context qualities, and retrievers. For instance, while some models are robust to noisy contexts, monotonically performing better with more contexts, others are more noise-sensitive and can effectively use only a few contexts before declining in performance. This framework also provides a deeper analysis of these differences by evaluating the LMs' sensitivity to signal and noise under specific context quality conditions. Using RAGGED, researchers and practitioners can derive actionable insights about how to optimally configure their RAG systems for their specific question-answering tasks.

Problem

• Evaluating RAG system performance under varied configurations
• Assessing reader robustness impact on RAG stability and scalability
• Analyzing retriever-reader interactions across diverse datasets

Innovation

• Framework for evaluating RAG configurations systematically
• Reader robustness key to RAG stability and scalability
• New metrics assess RAG stability and scalability