标题： 长上下文与RAG对于LLMs：评估与重新审视 显示英文标题

标题： Long Context vs. RAG for LLMs: An Evaluation and Revisits

摘要： 扩展上下文窗口（即长上下文，LC）和使用检索器有选择地访问相关信息（即检索增强生成，RAG）是使大语言模型（LLMs）能够整合极长外部上下文的两种主要策略。本文重新审视了这一领域的最新研究，强调了它们的关键见解和差异。然后我们通过过滤掉无需外部上下文即可回答的问题，对方法进行了更全面的评估，识别出最有效的检索方法，并扩展了数据集。我们表明，在问答基准测试中，LC通常优于RAG，尤其是在基于维基百科的问题上。基于摘要的检索表现与LC相当，而基于块的检索则落后。然而，RAG在基于对话和一般性问题查询中具有优势。这些见解突显了RAG和LC策略之间的权衡，为未来优化具有外部知识源的LLMs提供了指导。我们还对此话题进行了深入讨论，强调了现有研究中被忽视的上下文相关性的重要性。 显示英文摘要

摘要： Extending context windows (i.e., Long Context, LC) and using retrievers to selectively access relevant information (i.e., Retrieval-Augmented Generation, RAG) are the two main strategies to enable LLMs to incorporate extremely long external contexts. This paper revisits recent studies on this topic, highlighting their key insights and discrepancies. We then provide a more comprehensive evaluation by filtering out questions answerable without external context, identifying the most effective retrieval methods, and expanding the datasets. We show that LC generally outperforms RAG in question-answering benchmarks, especially for Wikipedia-based questions. Summarization-based retrieval performs comparably to LC, while chunk-based retrieval lags behind. However, RAG has advantages in dialogue-based and general question queries. These insights underscore the trade-offs between RAG and LC strategies, offering guidance for future optimization of LLMs with external knowledge sources. We also provide an in-depth discussion on this topic, highlighting the overlooked importance of context relevance in existing studies.