{"id":10544,"date":"2025-06-26T16:02:05","date_gmt":"2025-06-26T07:02:05","guid":{"rendered":"https:\/\/mtstlab.org\/?p=10544"},"modified":"2025-09-25T14:58:51","modified_gmt":"2025-09-25T05:58:51","slug":"12th-iciev%e3%81%a7%e7%99%ba%e8%a1%a8%e3%81%97%e3%81%be%e3%81%97%e3%81%9fb4-%e8%8c%82%e6%9c%a8","status":"publish","type":"post","link":"https:\/\/mtstlab.org\/?p=10544","title":{"rendered":"12th ICIEV\u3067\u767a\u8868\u3057\u307e\u3057\u305f(B4 \u8302\u6728)"},"content":{"rendered":"\n\u7686\u3055\u3093\u3053\u3093\u306b\u3061\u306f\uff0c B4\u306e\u8302\u6728\u3067\u3059\uff01
2025\u5e745\u670826\u65e5\uff5e5\u670829\u65e5\uff08\u30aa\u30f3\u30b5\u30a4\u30c8\uff1a\u5c0f\u5009\u56fd\u969b\u4f1a\u8b70\u5834\uff09\u306b\u3066\u958b\u50ac\u3055\u308c\u305fInternational Conference on Informatics, Electronics & Vision \uff08ICIEV)<\/a>\u3067\u767a\u8868\u3057\u307e\u3057\u305f\uff0e    \n\n\n\n\n\n\n\n\"\"\n\n

\u767a\u8868\u5185\u5bb9<\/h3>\n\n\u30bf\u30a4\u30c8\u30eb\uff1aAnalysis of the changes in the attitude of the news comments caused by knowing that the comments were generated by a large language model<\/strong>   \u6982\u8981<\/strong>\uff1aThis study investigated the attitudes of people toward texts generated by large language models (LLMs), such as social networking service posts and news comments. Recently, the number of people viewing texts generated by LLMs has increased. Because an LLM can generate natural texts that are almost indistinguishable from those written by humans, there is a concern that generating such natural texts may cause problems such as maliciously guiding public opinion. To evaluate the reception of LLM-generated texts, we conducted an experiment based on the hypothesis that the knowledge that a text was generated by an LLM would influence user acceptance. In the experiment, news comments in which AI-generated comments were mixed were shown to the participants. We controlled whether the user was aware that the text had been generated by an LLM, and assessed their viewpoints from four perspectives: perceived friendliness, trustworthiness, empathy, and reference. The results showed that a generated comment imitating the opinion of an expert increased in rank when it was disclosed that the LLM generated the comment. In particular, \u201creliability\u201d and \u201cinformative\u201d were sensitive to this disclosure, while \u201cfamiliar\u201d and \u201cempathy\u201d were not. This indicates that expert labeling significantly enhances perceived reliability, and the finding raises concerns about the possibility of news viewers being implicitly guided to a particular opinion.   \n\n \n\n