Why Does ChatGPT Fall Short in Providing Truthful Answers?

TL;DR

This paper investigates why ChatGPT struggles with truthful answers, focusing on factuality issues related to knowledge memorization and recall, and proposes strategies to improve its factual accuracy.

Contribution

It provides a detailed analysis of ChatGPT's failures in factuality and suggests enhancement strategies involving external knowledge and recall cues.

Findings

Factuality is the primary failure mode in ChatGPT's answers.

Augmenting with external knowledge improves factual accuracy.

Using cues for knowledge recall enhances model performance.

Abstract

Recent advancements in large language models, such as ChatGPT, have demonstrated significant potential to impact various aspects of human life. However, ChatGPT still faces challenges in providing reliable and accurate answers to user questions. To better understand the model's particular weaknesses in providing truthful answers, we embark an in-depth exploration of open-domain question answering. Specifically, we undertake a detailed examination of ChatGPT's failures, categorized into: comprehension, factuality, specificity, and inference. We further pinpoint factuality as the most contributing failure and identify two critical abilities associated with factuality: knowledge memorization and knowledge recall. Through experiments focusing on factuality, we propose several potential enhancement strategies. Our findings suggest that augmenting the model with granular external knowledge and cues for knowledge recall can enhance the model's factuality in answering questions.