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題名 LLM Agent for Disinformation Detection Based on DISARM Framework
作者 沈錳坤
Shan, Man-Kwan;Tseng, Kevin
貢獻者 資訊系
日期 2024-06
上傳時間 2025-01-23
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.
關聯 International AAAI Conference on Web and Social Media Workshop: Digital State Sponsored Disinformation and Propaganda, AAAI
資料類型 conference
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