Insights into Natural Language Database Query Errors: From Attention Misalignment to User Handling Strategies
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911775200378880 |
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| author | Ning, Zheng Tian, Yuan Zhang, Zheng Zhang, Tianyi Li, Toby |
| author_facet | Ning, Zheng Tian, Yuan Zhang, Zheng Zhang, Tianyi Li, Toby |
| contents | Querying structured databases with natural language (NL2SQL) has remained a difficult problem for years. Recently, the advancement of machine learning (ML), natural language processing (NLP), and large language models (LLM) have led to significant improvements in performance, with the best model achieving ~85% percent accuracy on the benchmark Spider dataset. However, there is a lack of a systematic understanding of the types, causes, and effectiveness of error-handling mechanisms of errors for erroneous queries nowadays. To bridge the gap, a taxonomy of errors made by four representative NL2SQL models was built in this work, along with an in-depth analysis of the errors. Second, the causes of model errors were explored by analyzing the model-human attention alignment to the natural language query. Last, a within-subjects user study with 26 participants was conducted to investigate the effectiveness of three interactive error-handling mechanisms in NL2SQL. Findings from this paper shed light on the design of model structure and error discovery and repair strategies for natural language data query interfaces in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07304 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Insights into Natural Language Database Query Errors: From Attention Misalignment to User Handling Strategies Ning, Zheng Tian, Yuan Zhang, Zheng Zhang, Tianyi Li, Toby Human-Computer Interaction Querying structured databases with natural language (NL2SQL) has remained a difficult problem for years. Recently, the advancement of machine learning (ML), natural language processing (NLP), and large language models (LLM) have led to significant improvements in performance, with the best model achieving ~85% percent accuracy on the benchmark Spider dataset. However, there is a lack of a systematic understanding of the types, causes, and effectiveness of error-handling mechanisms of errors for erroneous queries nowadays. To bridge the gap, a taxonomy of errors made by four representative NL2SQL models was built in this work, along with an in-depth analysis of the errors. Second, the causes of model errors were explored by analyzing the model-human attention alignment to the natural language query. Last, a within-subjects user study with 26 participants was conducted to investigate the effectiveness of three interactive error-handling mechanisms in NL2SQL. Findings from this paper shed light on the design of model structure and error discovery and repair strategies for natural language data query interfaces in the future. |
| title | Insights into Natural Language Database Query Errors: From Attention Misalignment to User Handling Strategies |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2402.07304 |