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.

大型语言模型近年来在各个领域取得了显著成就，然而，知识更新的及时性和成本以及语言模型的幻觉问题等因素限制了其在知识密集型任务中的应用，而检索增强生成可以提供帮助。因此，本研究提出了一种名为MetRag的多层思维增强的检索增强生成框架，综合了相似性思维和效用思维，并将大型语言模型用作任务自适应摘要工具，以提升检索增强生成的紧凑性。通过对知识密集型任务的大量实验，证明了MetRag的优越性。

相似性不是唯一的需求：为具有多层思维的检索增强生成赋能