Related papers: Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT

Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT

URL: http://arxiv.org/abs/2302.10198v1
Date: Sun, 19 Feb 2023 12:29:33 GMT
Title: Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT
Authors: Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du and Dacheng Tao
Abstract summary: ChatGPT has attracted great attention, as it can generate fluent and high-quality responses to human inquiries. We evaluate ChatGPT's understanding ability by evaluating it on the most popular GLUE benchmark, and comparing it with 4 representative fine-tuned BERT-style models. We find that: 1) ChatGPT falls short in handling paraphrase and similarity tasks; 2) ChatGPT outperforms all BERT models on inference tasks by a large margin; 3) ChatGPT achieves comparable performance compared with BERT on sentiment analysis and question answering tasks.
Score: 103.57103957631067
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Recently, ChatGPT has attracted great attention, as it can generate fluent and high-quality responses to human inquiries. Several prior studies have shown that ChatGPT attains remarkable generation ability compared with existing models. However, the quantitative analysis of ChatGPT's understanding ability has been given little attention. In this report, we explore the understanding ability of ChatGPT by evaluating it on the most popular GLUE benchmark, and comparing it with 4 representative fine-tuned BERT-style models. We find that: 1) ChatGPT falls short in handling paraphrase and similarity tasks; 2) ChatGPT outperforms all BERT models on inference tasks by a large margin; 3) ChatGPT achieves comparable performance compared with BERT on sentiment analysis and question answering tasks. Additionally, several bad cases from inference tasks show the potential limitation of ChatGPT.

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