RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems

• URL: http://arxiv.org/abs/2403.09040v2
• Date: Mon, 12 Aug 2024 17:12:04 GMT
• Title: RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems
• Authors: Jennifer Hsia, Afreen Shaikh, Zhiruo Wang, Graham Neubig,
• Abstract summary: 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.
• Score: 51.171355532527365
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Enhancing Retrieval-Augmented Generation: A Study of Best Practices [16.246719783032436]
  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.
  arXiv  Detail & Related papers  (2025-01-13T15:07:55Z)
• Re-ranking the Context for Multimodal Retrieval Augmented Generation [28.63893944806149]
  Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge to generate a response within a context. RAG systems face unique challenges: (i) the retrieval process may select irrelevant entries to user query (e.g., images, documents), and (ii) vision-language models or multi-modal language models like GPT-4o may hallucinate when processing these entries to generate RAG output. We show that by using a more advanced relevancy measure, one can enhance the retrieval process by selecting more relevant pieces from the knowledge-base and eliminate
  arXiv  Detail & Related papers  (2025-01-08T18:58:22Z)
• 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)
• Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphs [23.357843519762483]
  Recent studies have demonstrated that leveraging the Retrieval-Augmented Generation framework, combined with Knowledge Graphs, robustly enhances the reasoning capabilities of Large language models. We introduce a Multi-objective Multi-Armed Bandit enhanced RAG framework, supported by multiple retrieval methods with diverse capabilities. Our method significantly outperforms baseline methods in non-stationary settings while achieving state-of-the-art performance in stationary environments.
  arXiv  Detail & Related papers  (2024-12-10T15:56:03Z)
• SFR-RAG: Towards Contextually Faithful LLMs [57.666165819196486]
  Retrieval Augmented Generation (RAG) is a paradigm that integrates external contextual information with large language models (LLMs) to enhance factual accuracy and relevance. We introduce SFR-RAG, a small LLM that is instruction-textual with an emphasis on context-grounded generation and hallucination. We also present ConBench, a new evaluation framework compiling multiple popular and diverse RAG benchmarks.
  arXiv  Detail & Related papers  (2024-09-16T01:08:18Z)
• WeQA: A Benchmark for Retrieval Augmented Generation in Wind Energy Domain [2.8514947506989707]
  We present a comprehensive framework to generate a domain relevant RAG benchmark. Our framework is based on automatic question-answer generation with Human (domain experts)-AI Large Language Model (LLM) teaming. We demonstrate the framework by introducing WeQA, a first-of-its-kind benchmark on the wind energy domain.
  arXiv  Detail & Related papers  (2024-08-21T17:43:11Z)
• RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [69.4501863547618]
  This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios. With a focus on factual accuracy, we propose three novel metrics Completeness, Hallucination, and Irrelevance. Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
  arXiv  Detail & Related papers  (2024-08-02T13:35:11Z)
• 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)
• Generation-Augmented Retrieval for Open-domain Question Answering [134.27768711201202]
  Generation-Augmented Retrieval (GAR) for answering open-domain questions. We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy. GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader.
  arXiv  Detail & Related papers  (2020-09-17T23:08:01Z)

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.