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Updated: May 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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聊天机器人是可靠的文字注释器吗? 有时候有时候有时候有时候.

Ross Deans Kristensen-McLachlan1,2, Miceal Canavan3, Marton Kárdos2

  • 1Department of Linguistics, Cognitive Science, and Semiotics, Aarhus University, Aarhus 8000, Denmark.

PNAS nexus
|April 2, 2025
PubMed
概括
此摘要是机器生成的。

与ChatGPT相比,开源大型语言模型 (LLM) 在社会科学文本注释方面表现不同. 监督模型,如DistilBERT,通常提供更可靠的结果,特别是在开放科学实践中.

关键词:
自然语言处理自然语言处理.开放科学是一个开放的科学.数据注释数据注释大型语言模型.社会科学 社会科学 社会科学

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科学领域:

  • 社会科学 社会科学 社会科学
  • 计算语言学 计算语言学
  • 人工智能的人工智能

背景情况:

  • 聊天GPT对社会科学文本注释有希望,但有缺点 (闭源,透明度,可重复性,成本,数据保护).
  • 开源 (OS) 大型语言模型 (LLM) 为解决这些局限性提供了一个潜在的替代方案.

研究的目的:

  • 系统地比较OS LLMs与ChatGPT和传统的监督机器学习分类器在文本注释任务中的性能.
  • 评估零射击,少数射击学习和提示变化的对模型性能的影响.

主要方法:

  • 对多个OS的LLM和ChatGPT进行比较评估.
  • 利用零射击和少数射击学习与通用和自定义提示.
  • 在美国新闻媒体推特的新数据集上测试模型,用于二进制文本注释.
  • 与监督分类模型 (DistilBERT) 进行LLM性能比较.

主要成果:

  • 在ChatGPT和各种OS LLM之间,在不同的注释任务中观察到显著的性能差异.
  • 使用DistilBERT的监督分类器在总体上表现优于ChatGPT和评估的OS LLMs.
  • 发现ChatGPT的性能对于实质性文本注释是不可靠的.

结论:

  • 在社会科学研究中使用ChatGPT进行实质性文本注释时,建议谨慎使用,因为性能可变性和开放科学挑战.
  • OS LLMs提供了一个替代方案,但需要仔细评估;像DistilBERT这样的监督方法仍然是一个强大的基准.
  • 需要进一步的研究来优化OS LLMs的社会科学应用,并确保透明度和可重复性.