arXiv:2407.14767
공식 코드 · Papers with Code
I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL Generation
원문 초록
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
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
SQL-o1: Automatic Text-to-SQL Program Synthesis via Self-Reward Heuristic Search
XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL
Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task
Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA
SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation
Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.