聊天GPT和大型语言模型在学术界:机遇和挑战
Jesse G Meyer1, Ryan J Urbanowicz2, Patrick C N Martin2
1Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles California, USA. jesse.meyer@cshs.org.
BioData mining
|July 13, 2023
概括
大型语言模型 (LLM) 提供了重要的学术潜力,但需要仔细的伦理考虑. 研究人员必须学会有效地使用这些工具,解决未来学术应用的偏见和准确性问题.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 学术研究学术研究
背景情况:
- 2022年底大型语言模型 (LLM) 的出现标志着人工智能的重大进步.
- 基于LLM的聊天机器人可以生成类似人类的文本,为学术界提供机会和挑战.
研究的目的:
- 从学术角度评估LLM的实用性和局限性.
- 讨论在学术写作,教育和编程中使用LLM的伦理含义.
- 概述关于LLMs在学术环境中的整合立场.
主要方法:
- 编辑审查的LLM技术.
- 讨论LLM的能力和伦理考虑.
- 分析LLM对学术写作,教育和编程的影响.
主要成果:
- 法学士课程呈现了一个范式转变,有可能提高学术工作效率.
- 关键考虑因素包括公平使用,固有的偏见,准确性限制,以及产生抄袭内容的可能性.
- 有效的整合需要解决这些挑战.
结论:
- 学术界必须制定有效和道德使用法学士的策略.
- 使用LLM本质上不是抄袭,但用户必须验证输出的原创性和准确性.
- 量化LLM偏见并了解其局限性对于负责任的采用至关重要.
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