@inproceedings{zheng-etal-2025-data,
    title = "Data-Efficient Automatic Prompt Optimization for Memory-Enhanced Conversational Agents",
    author = "Zheng, Ervine  and
      Li, Yikuan  and
      Tso, Geoffrey Jay  and
      Kuang, Jilong",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.126/",
    pages = "1793--1804",
    ISBN = "979-8-89176-333-3",
    abstract = "Automatic prompt optimization (APO) uses algorithms to automatically refine prompts for LLMs, effectively reducing human effort in prompt engineering. However, applying APO to memory-enhanced conversational agents presents unique challenges. These agents leverage memory to retain information from historical interactions with users and provide context-aware and personalized responses. Optimizing prompts for these agents is challenging due to their complex, interconnected modules that include memory writing, reading, and response generation. This paper introduces a data-efficient framework for APO in these agents. Our approach leverages LLMs to holistically optimize the prompts of all agents. We also introduce an automated evaluation module that not only provides a holistic quality score for responses but also performs error attribution, pinpointing failures within the specific modules. More importantly, to ensure the evaluation module aligns with human judgment, we develop a data-efficient active sampling algorithm with convex optimization to select the most informative samples for human feedback and prompt improvement. We conducted experiments on two health-related conversation datasets to demonstrate the effectiveness of the proposed framework."
}