打造化学领域智能大脑，实现知识理解与推理决策。
KALE-LM-Chem: Vision and Practice Toward an AI Brain for Chemistry
- 构建四维化学智能框架：信息提取、语义解析、知识问答、推理规划。
- 推出KALE-LM-Chem系列模型，在化学任务中表现优异。
- 适合科研人员与AI开发者，推动化学科学智能化发展。
近期大型语言模型（LLMs）的发展展现出强大的领域智能潜力。本文提出构建化学智能大脑的愿景，将化学智能围绕四大核心能力展开：信息提取、语义解析、基于知识的问答及推理与规划。我们认为领域知识与逻辑是实现该系统辅助和加速科学发现的关键支柱。为此，我们推出了首个化学领域大语言模型：KALE-LM-Chem 和 KALE-LM-Chem-1.5，其在多项化学相关任务中取得了优异表现。本工作旨在为实现更智能的AI奠定基础，推动人类科技与社会发展。
原文摘要 · Abstract (English)
Recent advancements in large language models (LLMs) have demonstrated strong potential for enabling domain-specific intelligence. In this work, we present our vision for building an AI-powered chemical brain, which frames chemical intelligence around four core capabilities: information extraction, semantic parsing, knowledge-based QA, and reasoning & planning. We argue that domain knowledge and logic are essential pillars for enabling such a system to assist and accelerate scientific discovery. To initiate this effort, we introduce our first generation of large language models for chemistry: KALE-LM-Chem and KALE-LM-Chem-1.5, which have achieved outstanding performance in tasks related to the field of chemistry. We hope that our work serves as a strong starting point, helping to realize more intelligent AI and promoting the advancement of human science and technology, as well as societal development.
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