English

I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL Generation

Computation and Language 2024-10-01 v2 Artificial Intelligence

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

Keywords

Cite

@article{arxiv.2407.14767,
  title  = {I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL Generation},
  author = {Cheng-Kuang Wu and Zhi Rui Tam and Chao-Chung Wu and Chieh-Yen Lin and Hung-yi Lee and Yun-Nung Chen},
  journal= {arXiv preprint arXiv:2407.14767},
  year   = {2024}
}

Comments

Accepted by EMNLP 2024 Main Conference

R2 v1 2026-06-28T17:48:07.445Z