Multi-turn instruction following is essential for building intelligent conversational systems that can consistently adhere to instructions across dialogue turns. However, existing approaches to enhancing multi-turn instruction following primarily rely on collecting or generating large-scale multi-turn dialogue datasets to fine-tune large language models (LLMs), which treat each response generation as an isolated task and fail to explicitly incorporate multi-turn instruction following into the optimization objectives. As a result, instruction-tuned LLMs often struggle with complex long-distance constraints. In multi-turn dialogues, relational constraints across turns can be naturally modeled as labeled directed edges, making graph structures particularly suitable for modeling multi-turn instruction following. Despite this potential, leveraging graph structures to enhance the multi-turn instruction following capabilities of LLMs remains unexplored. To bridge this gap, we propose GraphIF, a plug-and-play framework that models multi-turn dialogues as directed relation graphs and leverages graph prompts to enhance the instruction following capabilities of LLMs. GraphIF comprises three key components: (1) an agent-based relation extraction module that captures inter-turn semantic relations via action-triggered mechanisms to construct structured graphs; (2) a relation graph prompt generation module that converts structured graph information into natural language prompts; and (3) a response rewriting module that refines initial LLM outputs using the generated graph prompts. Extensive experiments on two long multi-turn dialogue datasets demonstrate that GraphIF can be seamlessly integrated into instruction-tuned LLMs and leads to significant improvements across all four multi-turn instruction-following evaluation metrics.

翻译：多轮指令跟随对于构建能够在对话轮次间持续遵循指令的智能会话系统至关重要。然而，现有增强多轮指令跟随的方法主要依赖于收集或生成大规模多轮对话数据集来微调大语言模型（LLMs），这些方法将每次响应生成视为独立任务，未能将多轮指令跟随明确纳入优化目标。因此，经过指令微调的LLMs在处理复杂的长距离约束时常常面临困难。在多轮对话中，跨轮次的关系约束可以自然地建模为带标签的有向边，这使得图结构特别适合用于建模多轮指令跟随。尽管存在这一潜力，利用图结构来增强LLMs的多轮指令跟随能力仍未得到探索。为填补这一空白，我们提出了GraphIF，一个即插即用的框架，将多轮对话建模为有向关系图，并利用图提示来增强LLMs的指令跟随能力。GraphIF包含三个关键组件：（1）基于智能体的关系抽取模块，通过动作触发机制捕捉轮次间的语义关系以构建结构化图；（2）关系图提示生成模块，将结构化图信息转换为自然语言提示；（3）响应重写模块，利用生成的图提示优化LLMs的初始输出。在两个长多轮对话数据集上的大量实验表明，GraphIF可以无缝集成到经过指令微调的LLMs中，并在全部四项多轮指令跟随评估指标上带来显著提升。