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        <title>Is ChatGPT a General-Purpose Natural Language Processing Task Solver?</title>
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    <abstract>Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot—i.e., without adaptation on downstream data. Recently, the debut of ChatGPT has drawn a great deal of attention from the natural language processing (NLP) community due to the fact that it can generate high-quality responses to human input and self-correct previous mistakes based on subsequent conversations. However, it is not yet known whether ChatGPT can serve as a generalist model that can perform many NLP tasks zero-shot. In this work, we empirically analyze the zero-shot learning ability of ChatGPT by evaluating it on 20 popular NLP datasets covering 7 representative task categories. With extensive empirical studies, we demonstrate both the effectiveness and limitations of the current version of ChatGPT. We find that ChatGPT performs well on many tasks favoring reasoning capabilities (e.g., arithmetic reasoning) while it still faces challenges when solving specific tasks such as sequence tagging. We additionally provide in-depth analysis through qualitative case studies.</abstract>
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%0 Conference Proceedings
%T Is ChatGPT a General-Purpose Natural Language Processing Task Solver?
%A Qin, Chengwei
%A Zhang, Aston
%A Zhang, Zhuosheng
%A Chen, Jiaao
%A Yasunaga, Michihiro
%A Yang, Diyi
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F qin-etal-2023-chatgpt
%X Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot—i.e., without adaptation on downstream data. Recently, the debut of ChatGPT has drawn a great deal of attention from the natural language processing (NLP) community due to the fact that it can generate high-quality responses to human input and self-correct previous mistakes based on subsequent conversations. However, it is not yet known whether ChatGPT can serve as a generalist model that can perform many NLP tasks zero-shot. In this work, we empirically analyze the zero-shot learning ability of ChatGPT by evaluating it on 20 popular NLP datasets covering 7 representative task categories. With extensive empirical studies, we demonstrate both the effectiveness and limitations of the current version of ChatGPT. We find that ChatGPT performs well on many tasks favoring reasoning capabilities (e.g., arithmetic reasoning) while it still faces challenges when solving specific tasks such as sequence tagging. We additionally provide in-depth analysis through qualitative case studies.
%R 10.18653/v1/2023.emnlp-main.85
%U https://aclanthology.org/2023.emnlp-main.85
%U https://doi.org/10.18653/v1/2023.emnlp-main.85
%P 1339-1384

Markdown (Informal)

[Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://aclanthology.org/2023.emnlp-main.85) (Qin et al., EMNLP 2023)

• Is ChatGPT a General-Purpose Natural Language Processing Task Solver? (Qin et al., EMNLP 2023)

ACL

• Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023. Is ChatGPT a General-Purpose Natural Language Processing Task Solver?. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1339–1384, Singapore. Association for Computational Linguistics.