TY - GEN
T1 - Towards Robustness of Large Language Models on Text-to-SQL Task
T2 - 32nd International Conference on Artificial Neural Networks, ICANN 2023
AU - Zhang, Weixu
AU - Wang, Yu
AU - Fan, Ming
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Recent advances in large language models (LLMs) like ChatGPT have led to impressive results on various natural language processing (NLP) challenges including text-to-SQL task, which aims to automatically generate SQL queries from natural language questions. However, these language models are still subject to vulnerabilities such as adversarial attacks, domain shift and lack of robustness, which can greatly affect their performance and reliability. In this paper, we conduct a comprehensive evaluation of large language models, such as ChatGPT, on their robustness in text-to-SQL tasks. We assess the impact of adversarial and domain generalization perturbations on LLMs using seven datasets, five of which are popular robustness evaluation benchmarks for text-to-SQL tasks and two are synthetic adversarial datasets generated by ChatGPT. Our experiments show that while LLMs exhibit promise as zero-shot text-to-SQL parsers, their performances degrade under adversarial and domain generalization perturbations, with varying degrees of robustness depending on the type and level of perturbations applied. We also explore the impact of usage-related factors such as prompt design on the performance and robustness of LLMs. Our study provides insights into the limitations and potential directions for future research to enhance the performance and robustness of LLMs on text-to-SQL and other NLP tasks.
AB - Recent advances in large language models (LLMs) like ChatGPT have led to impressive results on various natural language processing (NLP) challenges including text-to-SQL task, which aims to automatically generate SQL queries from natural language questions. However, these language models are still subject to vulnerabilities such as adversarial attacks, domain shift and lack of robustness, which can greatly affect their performance and reliability. In this paper, we conduct a comprehensive evaluation of large language models, such as ChatGPT, on their robustness in text-to-SQL tasks. We assess the impact of adversarial and domain generalization perturbations on LLMs using seven datasets, five of which are popular robustness evaluation benchmarks for text-to-SQL tasks and two are synthetic adversarial datasets generated by ChatGPT. Our experiments show that while LLMs exhibit promise as zero-shot text-to-SQL parsers, their performances degrade under adversarial and domain generalization perturbations, with varying degrees of robustness depending on the type and level of perturbations applied. We also explore the impact of usage-related factors such as prompt design on the performance and robustness of LLMs. Our study provides insights into the limitations and potential directions for future research to enhance the performance and robustness of LLMs on text-to-SQL and other NLP tasks.
KW - Adversarial attacks
KW - ChatGPT
KW - Large language model
KW - Robustness
KW - text-to-SQL
UR - https://www.scopus.com/pages/publications/85174635474
U2 - 10.1007/978-3-031-44192-9_15
DO - 10.1007/978-3-031-44192-9_15
M3 - 会议稿件
AN - SCOPUS:85174635474
SN - 9783031441912
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 181
EP - 192
BT - Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings
A2 - Iliadis, Lazaros
A2 - Papaleonidas, Antonios
A2 - Angelov, Plamen
A2 - Jayne, Chrisina
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 26 September 2023 through 29 September 2023
ER -