Chain-of-Rank：增强边缘设备中面向领域特定RAG的大语言模型
Chain-of-Rank: Enhancing Large Language Models for Domain-Specific RAG in Edge Device
AI总结：
针对边缘设备上小规模LLM的领域特定RAG推理成本高、难学习的问题，提出Chain of Rank方法，通过排序外部文档可靠性替代复杂推理，在降本的同时保持高精度，取得基准测试SOTA结果。
摘要：
结合大语言模型（LLM）的检索增强生成（RAG）在精度要求极高的专业领域中极具价值。为了让LLM更适配目标领域，近期研究者开发了领域特定RAG，通过微调让LLM提前接触目标领域知识。领域特定RAG在边缘设备这类资源受限环境中更具实用价值，因为这类环境仅能使用小规模LLM，却需要可靠执行特定任务（如个性化任务）。尽管领域特定RAG在这一点上与边缘设备高度适配，但它通常依赖思维链（CoT）这类常用推理技术。推理步骤虽有助于理解给定的外部知识，但其计算成本高昂，且小规模LLM难以学习这类推理能力。针对这一问题，我们提出了排序链（Chain of Rank, CoR），将研究重点从复杂冗长的推理转向对输入外部文档可靠性的简单排序。CoR在保持高精度的同时降低了计算复杂度，尤其适用于资源受限环境。我们在多个基准测试中取得了当前最优（SOTA）结果，并对其有效性进行了分析。
英文摘要：
Retrieval-augmented generation (RAG) with large language models (LLMs) is especially valuable in specialized domains, where precision is critical. To more specialize the LLMs into a target domain, domain-specific RAG has recently been developed by allowing the LLM to access the target domain early via finetuning. The domain-specific RAG makes more sense in resource-constrained environments like edge devices, as they should perform a specific task (e.g. personalization) reliably using only small-scale LLMs. While the domain-specific RAG is well-aligned with edge devices in this respect, it often relies on widely-used reasoning techniques like chain-of-thought (CoT). The reasoning step is useful to understand the given external knowledge, and yet it is computationally expensive and difficult for small-scale LLMs to learn it. Tackling this, we propose the Chain of Rank (CoR) which shifts the focus from intricate lengthy reasoning to simple ranking of the reliability of input external documents. Then, CoR reduces computational complexity while maintaining high accuracy, making it particularly suited for resource-constrained environments. We attain the state-of-the-art (SOTA) results in benchmarks, and analyze its efficacy.
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