Scholarlink

Abstract <p>The article examines hallucinations that arise in two versions of the GPT large language model, GPT-3.5-turbo and GPT-4. The main objective is to investigate potential sources of hallucinations and to classify them, as well as developing strategies to address them. The study identifies issues that can lead to content generation that does not correspond to factual data and misleads users. The findings have practical significance for developers and users of language models due to the approaches proposed to enhance the quality and reliability of generated content.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Causes of Content Distortion: Analysis and Classification of Hallucinations in GPT Large Language Models

  • 期刊论文

  • 发表日期：

    2025年12月1日

查看全文
  • M. Sh. Madzhumder,
  • D. D. Begunova

摘要

Abstract

The article examines hallucinations that arise in two versions of the GPT large language model, GPT-3.5-turbo and GPT-4. The main objective is to investigate potential sources of hallucinations and to classify them, as well as developing strategies to address them. The study identifies issues that can lead to content generation that does not correspond to factual data and misleads users. The findings have practical significance for developers and users of language models due to the approaches proposed to enhance the quality and reliability of generated content.