标题： ChatSR：用于科学公式发现的多模态大语言模型 显示英文标题

标题： ChatSR: Multimodal Large Language Models for Scientific Formula Discovery

摘要： 公式是人类与自然之间交流的语言。 从观测数据中发现描述自然规律的公式是科学研究的目的。 这也是人工智能中的一个重要研究课题，称为符号回归问题。 现有的大多数符号回归方法直接从观测数据生成表达式。 尽管在某些方法中，我们可以通过添加约束或引入一些特殊的字符提示来将一些先验知识注入模型中。 然而，这些方法只能引入事先指定的有限量的先验知识。 更不用说理解自然语言指令了。 在本文中，基于多模态大语言模型强大的知识储备和语言理解能力，我们提出了 ChatSR，它就像一位博学的人类科学家，我们可以通过自然语言告诉它任何先验知识，以指导其公式生成。 通过在13个数据集上的测试，ChatSR不仅在传统的符号回归任务上表现出最先进的性能。 更值得注意的是，ChatSR能够很好地理解自然语言提示中包含的先验知识，并提高生成表达式的质量。 此外，令人兴奋的是，ChatSR具有良好的零样本能力，可以理解训练数据中不存在的先验知识。 显示英文摘要

摘要： Formulas are the language of communication between humans and nature. The discovery of formulas to describe natural laws from observational data is the purpose of scientific research. It is also an important research topic in artificial intelligence, which is called a symbolic regression problem. Most of the existing symbolic regression methods generate expressions directly from observed data. Although in some methods, we can inject some prior knowledge into the model by adding constraints or introducing some special character hints. However, these methods can only introduce a limited amount of prior knowledge specified in advance. Not to mention understanding natural language instructions. In this article, based on the powerful knowledge reserve and language understanding ability of multi-modal large language models, we present ChatSR, which acts like a knowledgeable human scientist, and we can tell it any prior knowledge through natural language to guide it in formula generation. By testing on 13 datasets, ChatSR not only shows state-of-the-art performance on traditional symbolic regression tasks. More notably, ChatSR can well understand the prior knowledge contained in natural language prompts and improve the quality of generated expressions. In addition, it is exciting that ChatSR has a good zero-shot capability to understand prior knowledge that is not present in the training data.