Generating Surprising and Diverse Ideas Using ChatGPT

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Generative AIs have become widespread and are being used for various purposes. However, a phenomenon called hallucination, in which generative AI outputs plausible lies, is being viewed as a problem. While hallucination is a problem when it is important to be factual, in some cases, such as idea generation, it is not a problem. In fact, in some cases, it may be better to have hallucination occur to come up with surprising and diverse ideas. In this study, we compared several prompts with the aim of getting ChatGPT to output information that differs from the training data for GPT when generating ideas for a fictional historical novel. The results showed that it was more effective and led to generation of more surprising ideas to directly instruct ChatGPT to include content that differs from the facts within the prompt rather than using adversarial prompts that cause hallucinations.

Original languageEnglish
Title of host publicationSustainability and Empowerment in the Context of Digital Libraries - 26th International Conference on Asia-Pacific Digital Libraries, ICADL 2024, Proceedings
EditorsGillian Oliver, Viviane Frings-Hessami, Jia Tina Du, Taro Tezuka
PublisherSpringer Science and Business Media Deutschland GmbH
Pages222-228
Number of pages7
ISBN (Print)9789819608645
DOIs
Publication statusPublished - 2025
Event26th International Conference on Asia-Pacific Digital Libraries, ICADL 2024 - Bandar Sunway, Malaysia
Duration: Dec 4 2024Dec 6 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15493 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Asia-Pacific Digital Libraries, ICADL 2024
Country/TerritoryMalaysia
CityBandar Sunway
Period12/4/2412/6/24

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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