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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Toward Eliminating Hallucinations: GPT-based Explanatory AI for Intelligent Textbooks and Documentation |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| ISSN | 1613-0073 |
| Page Range | 54 - 65 |
| Event Title | Tokyo’23: Fifth Workshop on Intelligent Textbooks (iTextbooks) at the 24th International Conference on Artificial Intelligence in Education (AIED’2023), |
| Event Type | workshop |
| Event Location | Tokyo, Japan |
| Event Start Date | July 3 - 2023 |
| Event End Date | July 7 - 2023 |
| Series Name | CEUR Workshop Proceedings |
| Number | 3444 |
| Publisher | CEUR-WS |
| Abstract Text | Traditional explanatory resources, such as user manuals and textbooks, often contain content that may not cater to the diverse backgrounds and information needs of users. Yet, developing intuitive, user-centered methods to effectively explain complex or large amounts of information is still an open research challenge. In this paper we present ExplanatoryGPT, an approach we devised and implemented to transform textual documents into interactive, intelligent resources, capable of offering dynamic, personalized explanations. Our approach uses state-of-the-art question-answering technology to generate on-demand, expandable explanations, with the aim of allowing readers to efficiently navigate and comprehend static materials. ExplanatoryGPT integrates ChatGPT, a state-of-the-art language model, with Achinstein’s philosophical theory of explanations. By combining question generation and answer retrieval algorithms with ChatGPT, our method generates interactive, user-centered explanations, while mitigating common issues associated with ChatGPT, such as hallucinations and memory shortcomings. To showcase the effectiveness of our Explanatory AI, we conducted tests using a variety of sources, including a legal textbook and documentation of some health and financial software. Specifically, we provide several examples that illustrate how ExplanatoryGPT excels over ChatGPT in generating more precise explanations, accomplished through thoughtful macro-planning of explanation content. Notably, our approach also avoids the need to provide the entire context of the explanation as a prompt to ChatGPT, a process that is often not feasible due to common memory constraints. |
| Free access at | Official URL |
| Official URL | https://ceur-ws.org/Vol-3444/itb23_s3p2.pdf |
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