EMNLP 2024main1 citations
I Need Help! Evaluating LLM’s Ability to Ask for Users’ Support: A Case Study on Text-to-SQL Generation
Cheng-Kuang Wu, Zhi Rui Tam, Chao-Chung Wu, Chieh-Yen Lin, Hung-yi Lee, Yun-Nung Chen
Abstract
This study explores the proactive ability of LLMs to seek user support. We propose metrics to evaluate the trade-off between performance improvements and user burden, and investigate whether LLMs can determine when to request help under varying information availability. Our experiments show that without external feedback, many LLMs struggle to recognize their need for user support. The findings highlight the importance of external signals and provide insights for future research on improving support-seeking strategies. Source code: https://github.com/appier-research/i-need-help
BibTeX
@inproceedings{wu-etal-2024-need,
title = "{I} Need Help! Evaluating {LLM}`s Ability to Ask for Users' Support: A Case Study on Text-to-{SQL} Generation",
author = "Wu, Cheng-Kuang and
Tam, Zhi Rui and
Wu, Chao-Chung and
Lin, Chieh-Yen and
Lee, Hung-yi and
Chen, Yun-Nung",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.131/",
doi = "10.18653/v1/2024.emnlp-main.131",
pages = "2191--2199"
}