Large language models (LLMs) such as GPT-3 and ChatGPT have recently demonstrated impressive results across a wide range of tasks. LLMs are still limited, however, in that they frequently fail at complex reasoning, their reasoning processes are opaque, they are prone to 'hallucinate' facts, and there are concerns about their underlying biases. Letting models verbalize reasoning steps as natural language, a technique known as chain-of-thought prompting, has recently been proposed as a way to address some of these issues. Here we present the first release of ThoughtSource, a meta-dataset and software library for chain-of-thought (CoT) reasoning. The goal of ThoughtSource is to improve future artificial intelligence systems by facilitating qualitative understanding of CoTs, enabling empirical evaluations, and providing training data. This first release of ThoughtSource integrates six scientific/medical, three general-domain and five math word question answering datasets.

翻译：诸如GPT-3和ChatGPT等大型语言模型近期在广泛任务中展现出令人瞩目的成果。然而，这些模型仍存在局限性，例如在复杂推理任务中频繁出错、推理过程不透明、易产生事实'幻觉'，以及其潜在偏见引发担忧。通过自然语言表述推理步骤（即思维链提示技术）近期被提出作为解决上述部分问题的方法。本文推出ThoughtSource的首个版本——这是一个面向思维链推理的元数据集与软件库。ThoughtSource旨在通过促进对思维链的定性理解、支持实证评估并提供训练数据，从而改进未来人工智能系统。该首个版本整合了六个科学/医学、三个通用领域及五个数学文字问题问答数据集。