Summary of I Need Help! Evaluating Llm’s Ability to Ask For Users’ Support: a Case Study on Text-to-sql Generation, by Cheng-kuang Wu et al.
I Need Help! Evaluating LLM’s Ability to Ask for Users’ Support: A Case Study on Text-to-SQL Generation
by Cheng-Kuang Wu, Zhi Rui Tam, Chao-Chung Wu, Chieh-Yen Lin, Hung-yi Lee, Yun-Nung Chen
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new study examines the ability of Large Language Models (LLMs) to seek user support proactively. The researchers propose metrics to measure the trade-off between performance improvements and user burden, and investigate whether LLMs can determine when to request help under varying information availability. The experiments show that many LLMs struggle to recognize their need for user support without external feedback, highlighting the importance of external signals and providing insights for future research on improving support-seeking strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how big language models ask for help from users. Scientists created new ways to measure how well these models do when they need help, and tried to figure out if they can tell when they need it. The results show that many of these models have trouble knowing when they need help unless someone tells them what to do. This is important because it helps us understand how we can make language models better at asking for help. |
