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

ACL ARR 2025 February Submission3897 Authors

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 interaction chains. Our results highlight a fundamental limitation of current LLMs, restricting their ability to collaborate effectively in long interactions.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: benchmarking, NLP datasets

Contribution Types: Model analysis & interpretability, Data resources

Languages Studied: English

Submission Number: 3897