从工具到队友：评估大语言模型在多轮编程协作中的表现

From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions

February 19, 2025

作者: Nathanaël Carraz Rakotonirina, Mohammed Hamdy, Jon Ander Campos, Lucas Weber, Alberto Testoni, Marzieh Fadaee, Sandro Pezzelle, Marco Del Tredici

cs.AI

摘要

大型语言模型（LLMs）在工作环境中正被日益广泛地应用于各类任务，其在独立解决单一问题方面表现卓越。然而，它们是否也能在长期互动中有效协作呢？为探究此问题，我们引入了MemoryCode，一个合成的多会话数据集，旨在测试LLMs在模拟真实环境中追踪并执行简单编码指令的能力，同时处理无关信息。尽管所有测试模型均能良好处理孤立指令，但即便是如GPT-4o这样的顶尖模型，在指令分散于多个会话时，其表现也会显著下降。我们的分析表明，这归因于它们无法有效检索并整合长指令链中的信息。研究结果揭示了当前LLMs的一个根本性局限，限制了其在长期互动中有效协作的能力。

English

Large Language Models (LLMs) are increasingly used in working environments for a wide range of tasks, excelling at solving individual problems in isolation. However, are they also able to effectively collaborate over long-term interactions? To investigate this, we introduce MemoryCode, a synthetic multi-session dataset designed to test LLMs' ability to track and execute simple coding instructions amid irrelevant information, simulating a realistic setting. While all the models we tested handle isolated instructions well, even the performance of state-of-the-art models like GPT-4o deteriorates when instructions are spread across sessions. Our analysis suggests this is due to their failure to retrieve and integrate information over long instruction chains. Our results highlight a fundamental limitation of current LLMs, restricting their ability to collaborate effectively in long interactions.