Original Paper: https://arxiv.org/pdf/2302.10198

By: Qihuang Zhong, Liang Ding<,[, Juhua Liu, Bo Du, Dacheng Tao

Abstract:

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, by combining some advanced prompting strategies, we show that the understanding ability of ChatGPT can be further improved.

Summary Notes

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Can ChatGPT Understand as Well as It Generates Text? A Comparative Analysis with Fine-tuned BERT

The intrigue surrounding ChatGPT's natural language understanding (NLU) abilities, compared to other AI models like BERT, is significant in the field of artificial intelligence.

This blog post examines how ChatGPT stacks up against fine-tuned BERT models in NLU tasks, using the GLUE benchmark for evaluation.

Introduction

ChatGPT, based on OpenAI's InstructGPT, has made waves with its text generation skills. Yet, questions about its understanding capabilities linger, especially when compared to models like BERT.

Unraveling these differences is essential for AI Engineers seeking the best AI tools for their projects. This study aims to compare ChatGPT's performance in NLU tasks with that of fine-tuned BERT models.

Methodology