思想源:一个大型语言模型推理数据的中心枢纽
Simon Ott1, Konstantin Hebenstreit1, Valentin Liévin2
1Institute of Artificial Intelligence, Medical University of Vienna, Vienna, Austria.
Scientific data
|August 8, 2023
概括
ThoughtSource是一个新的数据集和库,用于改进人工智能 (AI) 推理. 它帮助人工智能模型语言化思维步骤,解决诸如事实错误和大型语言模型 (LLM) 中偏见等局限性问题.
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.
- 机器学习 机器学习
背景情况:
- 大型语言模型 (LLM) 显示出强的性能,但与复杂的推理,透明度,事实准确性和偏见作斗争.
- 思想链 (Chain-of-thought,CoT) 提示,模型将推理步骤口头化,是解决这些LLM局限性的解决方案之一.
研究的目的:
- 介绍ThoughtSource,这是一个全面的元数据集和用于CoT推理的软件库.
- 促进定性理解,经验评估,并为CoT提供培训数据.
- 推进开发更有能力,更可靠的人工智能系统.
主要方法:
- 开发了ThoughtSource,这是一个元数据集,集成了各种各样的问答数据集.
- 包含的数据集涵盖科学/医学,一般领域和数学词题.
- 提供了一个软件库来支持使用和分析CoT数据.
主要成果:
- ThoughtSource的初始版本包括15个数据集 (7个科学/医疗,3个一般,5个数学).
- 建立了一个基础资源,用于研究和增强AI中的CoT推理.
结论:
- ThoughtSource旨在通过提供标准化数据和工具来加速人工智能推理的进展.
- 让研究人员能够更好地理解和提高LLMs的推理能力.
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