@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Data-Efficient Automatic Prompt Optimization for Memory-Enhanced Conversational Agents
%A Zheng, Ervine
%A Li, Yikuan
%A Tso, Geoffrey Jay
%A Kuang, Jilong
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F zheng-etal-2025-data
%X 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.
%U https://aclanthology.org/2025.emnlp-industry.126/
%P 1793-1804
Markdown (Informal)
[Data-Efficient Automatic Prompt Optimization for Memory-Enhanced Conversational Agents](https://aclanthology.org/2025.emnlp-industry.126/) (Zheng et al., EMNLP 2025)
ACL