Why Does ChatGPT Fall Short in Providing Truthful Answers?

NeurIPS 2023 Workshop ICBINB Submission10 Authors

Published: 27 Oct 2023, Last Modified: 01 Dec 2023ICBINB 2023EveryoneRevisionsBibTeX
Keywords: Large Language Model, Question Answering
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.
Submission Number: 10
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