JSAI2024

Presentation information

Organized Session

Organized Session » OS-26

[1G4-OS-26a] OS-26

Tue. May 28, 2024 3:00 PM - 4:40 PM Room G (Room 22+23)

オーガナイザ:福田 賢一郎(産業技術総合研究所)、江上 周作(産業技術総合研究所)、宮田 なつき(産業技術総合研究所)、Qiu Yue(産業技術総合研究所)、鵜飼 孝典(富士通株式会社)、古崎 晃司(大阪電気通信大学)、川村 隆浩(農業・食品産業技術総合研究機構)、市瀬 龍太郎(東京工業大学)、岡田 慧(東京大学)

3:00 PM - 3:20 PM

[1G4-OS-26a-01] A System to Show Evidence for Reason of Dangerous Behaviors in Home Generated by Sentence Generation AI

〇Fumikatsu Anaguchi1, Takeshi Morita1,2 (1. Aoyama Gakuin University, 2. National Institute of Advanced Industrial Science and Technology)

Keywords:generative AI, Retrieval Augmented Generation, explainable AI, Large Language Model, Knowledge Graph

To use sentence generation AI safely and securely, it is necessary to show evidence citing the literatures. Therefore, in this work, we propose a system to show evidence for reason of dangerous behaviors in home generated by sentence generation AI for the dataset presented by Knowledge Graph Reasoning Challenge 2023.
First, we extract dangerous behaviors in home. Second, Sentence Generation AI generate reasons of it. Third, Retrieval Augmented Generation (RAG) retrieves a sentence similar to this reason from the literatures on dangerous behaviors in Home and shows the user as evidence.
We did a survey to evaluate whether the sentence generation AI can appropriately generate reasons for dangerous behaviors in the home, and whether the evidence showed by the proposed system for these reasons is appropriate.
As a result, average score of five-stage evaluation were 3.6 and 2.6. It is found that the proposed system can show general evidence for reasons.

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