ThoughtSource：大语言模型推理数据的中心枢纽
ThoughtSource: A central hub for large language model reasoning data
AI总结：
本文提出ThoughtSource元数据集与软件库，整合多领域思维链推理数据，以促进对CoT的定性理解、实证评估和训练数据供给，从而改进未来AI系统。
摘要：
大语言模型（LLMs），如GPT-4，近期在广泛任务中展现出令人印象深刻的结果。然而，LLMs仍然存在局限：它们经常在复杂推理中失败，其推理过程不透明，容易“幻觉”事实，并且其潜在偏见引发担忧。让模型以自然语言表达推理步骤，这一被称为思维链提示的技术，最近被提出用于解决其中一些问题。在此，我们提出ThoughtSource，一个用于思维链（CoT）推理的元数据集和软件库。ThoughtSource的目标是通过促进对CoT的定性理解、支持实证评估以及提供训练数据，来改进未来的人工智能系统。ThoughtSource的首个版本整合了七个科学/医学、三个通用领域和五个数学应用题问答数据集。
英文摘要：
Large language models (LLMs) such as GPT-4 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 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 seven scientific/medical, three general-domain and five math word question answering datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
