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TitleLLM Agent for Disinformation Detection Based on DISARM Framework
Creator沈錳坤
Shan, Man-Kwan;Tseng, Kevin
Contributor資訊系
Date2024-06
Date Issued2025-01-23
SummaryThe rise of AI-generated disinformation driven by large language models (LLMs) exacerbates threats to information trust and democracy. Motivated by the need for efficient detection, this paper integrates the DISARM framework with LLMs to enhance capabilities. The proposed OSINT LLM Agent leverages DISARM's structure and LLMs' reasoning for an effective, adaptable solution against disinformation campaigns. Experiments using real-world cases demonstrate feasibility and effectiveness, highlighting DISARM-LLM integration's potential for advanced detection.
RelationInternational AAAI Conference on Web and Social Media Workshop: Digital State Sponsored Disinformation and Propaganda, AAAI
Typeconference
DOI https://doi.org/10.36190/2024.68
dc.contributor 資訊系
dc.creator (作者) 沈錳坤
dc.creator (作者) Shan, Man-Kwan;Tseng, Kevin
dc.date (日期) 2024-06
dc.date.accessioned 2025-01-23-
dc.date.available 2025-01-23-
dc.date.issued (上傳時間) 2025-01-23-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/155307-
dc.description.abstract (摘要) The rise of AI-generated disinformation driven by large language models (LLMs) exacerbates threats to information trust and democracy. Motivated by the need for efficient detection, this paper integrates the DISARM framework with LLMs to enhance capabilities. The proposed OSINT LLM Agent leverages DISARM's structure and LLMs' reasoning for an effective, adaptable solution against disinformation campaigns. Experiments using real-world cases demonstrate feasibility and effectiveness, highlighting DISARM-LLM integration's potential for advanced detection.
dc.format.extent 529776 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) International AAAI Conference on Web and Social Media Workshop: Digital State Sponsored Disinformation and Propaganda, AAAI
dc.title (題名) LLM Agent for Disinformation Detection Based on DISARM Framework
dc.type (資料類型) conference
dc.identifier.doi (DOI) 10.36190/2024.68
dc.doi.uri (DOI) https://doi.org/10.36190/2024.68