用角色令牌让一个大模型高效完成多个检索生成任务
Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization
- 通过角色令牌区分不同任务，统一驱动一个大模型
- 在五个开放域问答数据集上均表现优异
- 适合需要多任务整合的轻量化部署场景
现有研究在查询理解、检索优化等子任务上分别优化了检索增强生成（RAG），但将这些优化整合进统一框架仍具挑战。本文提出 RoleRAG，一种基于角色特定令牌优化的统一 RAG 框架，通过六个模块分别处理 RAG 过程中的具体子任务。引入查询图以表示查询的分解结构，并可依据分解状态动态解析。所有模块由同一底层大模型驱动，通过任务特异的角色令牌进行区分并独立优化。该设计使 RoleRAG 能在单个 LLM 实例中动态激活不同模块，实现部署简化与资源节约。在五个开放域问答数据集上的实验表明，该框架具备有效性、泛化性和灵活性。
原文摘要 · Abstract (English)
Existing studies have optimized retrieval-augmented generation (RAG) across various sub-tasks, such as query understanding and retrieval refinement, but integrating these optimizations into a unified framework remains challenging. To tackle this problem, this work proposes RoleRAG, a unified RAG framework that achieves efficient multi-task processing through role-specific token optimization. RoleRAG comprises six modules, each handling a specific sub-task within the RAG process. Additionally, we introduce a query graph to represent the decomposition of the query, which can be dynamically resolved according to the decomposing state. All modules are driven by the same underlying LLM, distinguished by task-specific role tokens that are individually optimized. This design allows RoleRAG to dynamically activate different modules within a single LLM instance, thereby streamlining deployment and reducing resource consumption. Experimental results on five open-domain question-answering datasets demonstrate the effectiveness, generalizability, and flexibility of our framework.
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