\label{sec:abstract} 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 SciGLM, a suite of scientific language models able to conduct 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 SciInstruct, a diverse and high-quality dataset encompassing mathematics, physics, chemistry, and formal proofs. We fine-tuned the ChatGLM family of language models with SciInstruct, enhancing their capabilities in scientific and mathematical reasoning. Remarkably, SciGLM consistently improves both the base model (ChatGLM3-6B-Base) and larger-scale models (12B and 32B), without sacrificing the language understanding capabilities of the base model. This makes SciGLM a suitable foundational model to facilitate diverse scientific discovery tasks. For the benefit of the wider research community, we release SciInstruct, SciGLM, alongside a self-reflective framework and fine-tuning code at \url{https://github.com/THUDM/SciGLM}.

翻译：大型语言模型在辅助科学发现方面展现出潜力，但当前应用受限于模型对复杂科学概念的理解、符号方程推导及高级数值计算能力的不足。为弥合这些差距，我们提出SciGLM——一套能够执行大学水平科学推理的科学语言模型。其核心创新在于一种自反思指令标注框架，旨在解决科学领域的数据稀缺问题。该框架利用现有语言模型为未标注科学问题生成逐步推理过程，并通过自反思评估与修正机制进行优化。基于此框架，我们构建了覆盖数学、物理、化学及形式化证明的高质量多样化数据集SciInstruct。通过在该数据集上对ChatGLM系列语言模型进行微调，显著增强了其科学推理与数学推理能力。值得注意的是，SciGLM在保持基础模型语言理解能力的前提下，持续改进了基座模型（ChatGLM3-6B-Base）及更大规模模型（12B和32B）的性能，使其成为推动多样化科学发现任务的理想基础模型。为惠及更广泛的研究社区，我们在\url{https://github.com/THUDM/SciGLM}开源了SciInstruct、SciGLM、自反思框架及微调代码。