A Categorical Archive of ChatGPT Failures

TL;DR

This paper categorizes and analyzes the various failure modes of ChatGPT, highlighting its limitations and societal implications to guide future improvements in language models.

Contribution

It provides a comprehensive taxonomy of ChatGPT failures across eleven categories, which was previously lacking in the literature.

Findings

Identified eleven categories of ChatGPT failures

Discussed risks and societal implications of these failures

Provided insights to improve future language models

Abstract

Large language models have been demonstrated to be valuable in different fields. ChatGPT, developed by OpenAI, has been trained using massive amounts of data and simulates human conversation by comprehending context and generating appropriate responses. It has garnered significant attention due to its ability to effectively answer a broad range of human inquiries, with fluent and comprehensive answers surpassing prior public chatbots in both security and usefulness. However, a comprehensive analysis of ChatGPT's failures is lacking, which is the focus of this study. Eleven categories of failures, including reasoning, factual errors, math, coding, and bias, are presented and discussed. The risks, limitations, and societal implications of ChatGPT are also highlighted. The goal of this study is to assist researchers and developers in enhancing future language models and chatbots.