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GPTRC:用于评估大型语言模型的问答能力和局限性的大型数据集

Akarsh Tripathi¹, Tanisha Gupta¹, Ashutosh Kumar Dubey¹

¹Chitkara University School of Engineering and Technology, Chitkara University, Baddi, Himachal Pradesh, India.

|December 16, 2025

概括

一个新的数据集,即生成预训练变压器 (GPT) 响应库 (GPTRC),为分析大型语言模型 (LLM) 提供了50134个聊天GPT响应. 该资源有助于研究人员在各种任务和条件中评估LLM绩效.

科学领域:

自然语言处理自然语言处理.
人工智能的人工智能
机器学习机器学习

背景情况:

像ChatGPT这样的大型语言模型 (LLM) 展示了先进的功能,但需要全面的评估.
现有的数据集可能无法完全捕捉各种领域和问题类型中LLM绩效的细微差别.

研究的目的:

引入生成预训练变压器 (GPT) 响应库 (GPTRC),这是一个用于分析ChatGPT性能的大规模数据集.
为LLM研究提供透明,可复制和公开可访问的资源.

主要方法:

通过OpenAI的API使用GPT-3.5 Turbo模型生成了50,134个问题答案对.
包括各种各样的主题,问题格式 (事实和基于推理的),以及多语言记录.
收集的元数据,如思考时间,响应时间,响应长度和每个交互的关键词.

主要成果:

该GPTRC数据集能够对LLM的优点和局限性进行细粒度分析.
元数据有助于对准准确性,效率,语言丰富性和一致性进行比较研究.
该数据集允许对不同条件下的模型性能进行细微洞察.

结论:

该GPTRC作为一个基础资源,以提高LLM的稳定性.
它支持研究人员对跨领域的LLM行为进行基准分析和理解.
该数据集通过提供全面的评估工具,指导未来人工智能系统的开发.

关键词:

对AI模型进行基准测试.聊天GPT-3.5 轮增压器生成性人工智能评估大型语言模型 (LLM)自然语言处理 (NLP)问题答案数据集的问题答案数据集

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