大型语言模型对抗扰乱的稳定性
Saeed S Alahmari1, Lawrence Hall2, Peter R Mouton2,3
1Department of Computer Science, Najran University, Najran, Saudi Arabia. ssalahmari@nu.edu.sa.
Scientific reports
|November 29, 2025
概括
大型语言模型 (LLM) 对文本干扰,如打字错误和单词替换很敏感. 这影响了它们在现实应用中的可靠性,影响了文本生成质量.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人工智能 (AI) 是一种人工智能.
背景情况:
- 大型语言模型 (LLM) 在NLP任务中表现出色,但是在清洁数据上接受训练.
- 在处理具有人为错误的文本时,LLM可能会失败,例如打字错误或更改的单词选择.
研究的目的:
- 调查LLM对文本干扰,特别是字体错误和单词替换的弹性.
- 评估这些干扰对各种模型中文本生成质量的影响.
主要方法:
- 利用两个公共数据集来模拟文本扰动.
- 评估了六个最先进的LLM,包括GPT-4o和LLaMA3.3-70B.
- 集中分析对文本生成的影响,与以往以分类为重点的研究区分开来.
主要成果:
- LLM对文本干扰的敏感性有所表现.
- 字体错误和字体替换导致生成的文本输出有明显的变化.
- 在测试的模型中,对文本生成的影响是显著的.
结论:
- 由于常见的文本变化,LLM的稳定性受到挑战.
- 这些发现突出了LLM在现实NLP应用中的潜在可靠性问题.
- 需要进一步的研究来提高LLM对噪音输入数据的弹性.
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