小模型与大模型协作，提升复杂问题问答的准确率
Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration
- 小模型拆解复杂问题，大模型反馈优化拆解能力
- 3B小模型超越32B冻结大模型的提问分解效果
- 无需顶尖大模型蒸馏，适配多种黑盒大模型
检索增强生成（RAG）系统在处理多跳问答任务时，常因上下文检索不相关和复杂推理能力有限而表现不佳。本文提出Collab-RAG，一种白盒小语言模型（SLM）与黑盒大语言模型（LLM）协同训练框架。小模型将复杂问题分解为子问题，提升检索准确性，并辅助黑盒大模型进行更有效的推理；同时，黑盒大模型提供反馈信号，优化小模型的分解能力。实验表明，仅依赖成本较低的黑盒大模型监督，无需前沿大模型蒸馏，Collab-RAG在五个多跳问答数据集上平均优于现有黑盒仅用及小模型微调基线1.8%-14.2%。特别地，经微调的3B小模型在问题分解上超越冻结的32B大模型，验证了该方法在提升复杂问题推理与检索效率上的优越性。代码已开源。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) systems often struggle to handle multi-hop question-answering tasks accurately due to irrelevant context retrieval and limited complex reasoning capabilities. We introduce Collab-RAG, a collaborative training framework that leverages mutual enhancement between a white-box small language model (SLM) and a blackbox large language model (LLM) for RAG. Specifically, the SLM decomposes complex queries into simpler sub-questions, thus enhancing the accuracy of the retrieval and facilitating more effective reasoning by the black-box LLM. Concurrently, the black-box LLM provides feedback signals to improve the SLM's decomposition capability. We observe that Collab-RAG relies solely on supervision from an affordable black-box LLM without additional distillation from frontier LLMs, yet demonstrates strong generalization across multiple black-box LLMs. Experimental evaluations across five multi-hop QA datasets demonstrate that Collab-RAG substantially outperforms existing black-box-only and SLM fine-tuning baselines by 1.8%-14.2% on average. In particular, our fine-tuned 3B SLM surpasses a frozen 32B LLM in question decomposition, highlighting the efficiency of Collab-RAG in improving reasoning and retrieval for complex questions. The code of Collab-RAG is available on https://github.com/ritaranx/Collab-RAG/.
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