相似性并非你所需要的全部：赋予检索增强生成多层思想
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
AI总结：
针对检索增强生成中过度依赖相似性导致性能下降的问题，提出MetRag框架，通过结合相似性与效用导向思想并利用LLM作为任务自适应摘要器提供紧凑性思想，显著提升了知识密集型任务的性能。
摘要：
近年来，大型语言模型（LLMs）在各个领域取得了显著成就。然而，知识更新的不及时性和高昂成本，加上LLMs的幻觉问题，限制了它们在知识密集型任务中的应用，而检索增强生成（RAG）可以提供帮助。尽管如此，现有的检索增强模型通常使用相似性作为查询和文档之间的桥梁，并遵循先检索后阅读的流程。在这项工作中，我们认为相似性并不总是万能药，完全依赖相似性有时会降低检索增强生成的性能。为此，我们提出了MetRag，一个多层思想增强的检索增强生成框架。首先，超越现有的面向相似性的思想，我们采用一个小规模效用模型，该模型从LLM中汲取监督信号以形成面向效用的思想，并通过综合结合相似性和效用导向的思想进一步构建出更智能的模型。此外，鉴于检索到的文档集往往非常庞大，孤立地使用它们难以捕捉它们之间的共性和特征，我们提出将LLM作为任务自适应摘要器，赋予检索增强生成面向紧凑性的思想。最后，利用前述阶段的多层思想，调用LLM进行知识增强生成。在知识密集型任务上的大量实验证明了MetRag的优越性。
英文摘要：
In recent years, large language models (LLMs) have made remarkable achievements in various domains. However, the untimeliness and cost of knowledge updates coupled with hallucination issues of LLMs have curtailed their applications in knowledge intensive tasks, where retrieval augmented generation (RAG) can be of help. Nevertheless, existing retrieval augmented models typically use similarity as a bridge between queries and documents and follow a retrieve then read procedure. In this work, we argue that similarity is not always the panacea and totally relying on similarity would sometimes degrade the performance of retrieval augmented generation. To this end, we propose MetRag, a Multi layEred Thoughts enhanced Retrieval Augmented Generation framework. To begin with, beyond existing similarity oriented thought, we embrace a small scale utility model that draws supervision from an LLM for utility oriented thought and further come up with a smarter model by comprehensively combining the similarity and utility oriented thoughts. Furthermore, given the fact that the retrieved document set tends to be huge and using them in isolation makes it difficult to capture the commonalities and characteristics among them, we propose to make an LLM as a task adaptive summarizer to endow retrieval augmented generation with compactness-oriented thought. Finally, with multi layered thoughts from the precedent stages, an LLM is called for knowledge augmented generation. Extensive experiments on knowledge-intensive tasks have demonstrated the superiority of MetRag.
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