{"id":671329,"date":"2025-05-25T12:50:22","date_gmt":"2025-05-25T09:50:22","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/large-language-model-accurately-predicts-online-chat-derailments\/"},"modified":"2025-05-25T12:50:22","modified_gmt":"2025-05-25T09:50:22","slug":"large-language-model-accurately-predicts-online-chat-derailments","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/large-language-model-accurately-predicts-online-chat-derailments\/","title":{"rendered":"Large language model accurately predicts online chat derailments"},"content":{"rendered":"
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\n Examples of conversational derailment on Wikipedia Talk Pages: On the left is an example of a discussion that concluded without derailment, while on the right is an example of a derailed conversation. In the case on the right, the final comment ends with an aggressive remark directed at the other person: “…wasting other people’s time is simply rude.” Credit: IEEE Access<\/i> (2025). DOI: 10.1109\/ACCESS.2025.3554548
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Online chat rooms and social<\/a> networking platforms frequently experience harmful behavior as discussions drift from their intended topics toward personal conflict. Traditional predictive models typically depend on platform-specific data, limiting their app<\/a>licability and increasing implementation costs.<\/p>\n

In a new study, researchers at the University of Tsukuba applied a zero-shot prediction method to LLMs to detect conversational derailments. The performance of various untrained LLMs was compared to that of a deep learning model trained on curated datasets. The results showed that untrained LLMs achieved comparable, and in some cases superior, accuracy.<\/p>\n

These findings, published<\/a> in the journal IEEE Access<\/i>, suggest that platform operators can implement effective moderation tools at reduced cost by leveraging general<\/a>-purpose LLMs, supporting healthier online communities across diverse platforms.<\/p>\n

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More information:<\/strong>
\n\t\t\t\t\t\t\t\t\t\t\t\tKenya Nonaka et al, Zero-Shot Prediction of Conversational Derailment With Large Language Models, IEEE Access<\/i> (2025). DOI: 10.1109\/ACCESS.2025.3554548<\/a><\/p>\n<\/p><\/div>\n

\n Provided by
\n University of Tsukuba
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\n Citation<\/strong>:
\n Large language model accurately predicts online chat derailments (2025, May 23)
\n retrieved 25 May 2025
\n from https:\/\/techxplore.com\/news<\/a>\/2025-05-large-language-accurately-online-chat.html\n <\/p>\n

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