I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL Generation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916413364502528 |
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| author | Wu, Cheng-Kuang Tam, Zhi Rui Wu, Chao-Chung Lin, Chieh-Yen Lee, Hung-yi Chen, Yun-Nung |
| author_facet | Wu, Cheng-Kuang Tam, Zhi Rui Wu, Chao-Chung Lin, Chieh-Yen Lee, Hung-yi Chen, Yun-Nung |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14767 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL Generation Wu, Cheng-Kuang Tam, Zhi Rui Wu, Chao-Chung Lin, Chieh-Yen Lee, Hung-yi Chen, Yun-Nung Computation and Language Artificial Intelligence 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 |
| title | I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL Generation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2407.14767 |