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

• URL: http://arxiv.org/abs/2502.13791v1
• Date: Wed, 19 Feb 2025 14:58:04 GMT
• Title: From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions
• Authors: Nathanaël Carraz Rakotonirina, Mohammed Hamdy, Jon Ander Campos, Lucas Weber, Alberto Testoni, Marzieh Fadaee, Sandro Pezzelle, Marco Del Tredici,
• Abstract summary: MemoryCode is a dataset designed to test Large Language Models' ability to track and execute simple coding instructions amid irrelevant information. Our results highlight a fundamental limitation of current LLMs, restricting their ability to collaborate effectively in long interactions.
• Score: 9.344348861402928
• License:
• Abstract: 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.

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