通过状态更新提示策略，显著提升大模型长对话中的记忆与效率。
A State-Update Prompting Strategy for Efficient and Robust Multi-turn Dialogue
- 引入状态重建与历史提醒机制，动态管理对话历史。
- 在HotpotQA上信息筛选得分提升32.6%，问答准确率增14.1%。
- 无需训练，适配长程对话任务，适合构建高效智能体。
大型语言模型在长周期多轮对话中面临信息遗忘和效率低下的问题。为此，我们提出一种无需训练的提示工程方法——状态更新多轮对话策略。该方法通过“状态重建”和“历史提醒”机制有效管理对话历史。在多个多跳问答数据集上表现优异，例如在HotpotQA上，核心信息过滤得分提升32.6%，下游问答得分提高14.1%，同时推理时间减少73.1%，令牌消耗降低59.4%。消融实验证实了两个组件的关键作用。本工作为优化大模型在长程交互中的表现提供了有效方案，并为构建更鲁棒智能体提供了新思路。
原文摘要 · Abstract (English)
Large Language Models (LLMs) struggle with information forgetting and inefficiency in long-horizon, multi-turn dialogues. To address this, we propose a training-free prompt engineering method, the State-Update Multi-turn Dialogue Strategy. It utilizes "State Reconstruction" and "History Remind" mechanisms to effectively manage dialogue history. Our strategy shows strong performance across multiple multi-hop QA datasets. For instance, on the HotpotQA dataset, it improves the core information filtering score by 32.6%, leading to a 14.1% increase in the downstream QA score, while also reducing inference time by 73.1% and token consumption by 59.4%. Ablation studies confirm the pivotal roles of both components. Our work offers an effective solution for optimizing LLMs in long-range interactions, providing new insights for developing more robust Agents.
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