SciInstruct：用于训练科学语言模型的自反思指令标注数据集
SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models
AI总结：
针对LLMs在复杂科学概念、符号推导和数值计算上的不足，提出自反思指令标注框架构建高质量科学数据集SciInstruct，微调多个模型显著提升其科学与数学推理能力且不损失语言理解能力。
摘要：
大型语言模型（LLMs）在辅助科学发现方面展现出潜力。然而，此类应用目前受限于LLMs在理解复杂科学概念、推导符号方程以及求解高级数值计算方面的缺陷。为弥合这些差距，我们引入了SciInstruct，这是一套用于训练具备大学水平科学推理能力的科学语言模型的科学指令。我们方法的核心是一个新颖的自反思指令标注框架，旨在解决科学领域的数据稀缺挑战。该框架利用现有的LLMs为未标记的科学问题生成逐步推理过程，随后进行自反思的批评与修订过程。应用该框架，我们精心构建了一个包含物理、化学、数学和形式证明的多样化高质量数据集。我们从多个有趣的视角（如领域、规模、来源、问题类型、答案长度等）对精心构建的SciInstruct进行了分析。为验证SciInstruct的有效性，我们使用SciInstruct对不同的语言模型进行了微调，即ChatGLM3（6B和32B）、Llama3-8B-Instruct和Mistral-7B: MetaMath，在不牺牲基础模型语言理解能力的前提下，增强了它们的科学与数学推理能力。我们在 https://github.com/THUDM/SciGLM 发布了所有代码和SciInstruct。
英文摘要：
Large Language Models (LLMs) have shown promise in assisting scientific discovery. However, such applications are currently limited by LLMs' deficiencies in understanding intricate scientific concepts, deriving symbolic equations, and solving advanced numerical calculations. To bridge these gaps, we introduce SciInstruct, a suite of scientific instructions for training scientific language models capable of college-level scientific reasoning. Central to our approach is a novel self-reflective instruction annotation framework to address the data scarcity challenge in the science domain. This framework leverages existing LLMs to generate step-by-step reasoning for unlabelled scientific questions, followed by a process of self-reflective critic-and-revise. Applying this framework, we curated a diverse and high-quality dataset encompassing physics, chemistry, math, and formal proofs. We analyze the curated SciInstruct from multiple interesting perspectives (e.g., domain, scale, source, question type, answer length, etc.). To verify the effectiveness of SciInstruct, we fine-tuned different language models with SciInstruct, i.e., ChatGLM3 (6B and 32B), Llama3-8B-Instruct, and Mistral-7B: MetaMath, enhancing their scientific and mathematical reasoning capabilities, without sacrificing the language understanding capabilities of the base model. We release all codes and SciInstruct at https://github.com/THUDM/SciGLM.
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。
