无需训练的LLM框架，通过子任务交互避免信息丢失
A Training-free LLM Framework with Interaction between Contextually Related Subtasks in Solving Complex Tasks
- 子任务间可主动请求信息或触发动作，解决上下文关联任务中的信息丢失问题
- 在WebShop和HotpotQA上使用GPT-3.5/GPT-4测试，性能优于现有无训练基线
- 引入轨迹记忆与执行摘要机制，支持任务恢复与精准交互定位
大型语言模型（LLMs）在解决复杂任务方面表现出色。近期研究将复杂任务分解为具有独立上下文的子任务，但部分上下文相关子任务在执行过程中可能因信息丢失导致重复操作或失败。为此，我们提出一种无需训练的框架，通过交互机制使子任务可通过发送请求查询特定信息或触发已完成子任务的动作。为实现交互，引入子任务轨迹记忆，支持在接收到交互请求时恢复已执行子任务。此外，提出一种新的执行中动作，生成简洁准确的子任务执行过程与结果描述，帮助后续子任务确定交互目标与请求内容。我们在交互式决策任务WebShop和多跳问答任务HotpotQA上，使用GPT-3.5与GPT-4进行评估，实验结果表明，该框架优于当前最先进的无训练基线方法。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown remarkable capabilities in solving complex tasks. Recent work has explored decomposing such tasks into subtasks with independent contexts. However, some contextually related subtasks may encounter information loss during execution, leading to redundant operations or execution failures. To address this issue, we propose a training-free framework with an interaction mechanism, which enables a subtask to query specific information or trigger certain actions in completed subtasks by sending requests. To implement interaction, we introduce a subtask trajectory memory to enable resumption of completed subtasks upon receiving interaction requests. Additionally, we propose a new action during execution, which generates a concise and precise description of execution process and outcomes of a subtask, to assist subsequent subtasks in determining interaction targets and requests. We evaluate our framework on interactive decision-making task WebShop and multi-hop question answering HotpotQA, with GPT-3.5 and GPT-4, and comparison results show that our framework outperforms the state-of-the-art training-free baselines.
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