标题： MLLM-SR：对话式符号回归基于多模态大语言模型 显示英文标题

标题： MLLM-SR: Conversational Symbolic Regression base Multi-Modal Large Language Models

摘要： 公式是人类与自然之间的交流语言。 从观测数据中找到表达式以反映数据中每个变量之间的关系，是人工智能的重要研究课题，这被称为符号回归问题。 现有的符号回归方法根据给定的观测数据直接生成表达式，我们无法要求算法根据已知的先验知识生成符合特定要求的表达式。 例如，表达式需要包含$\sin$或者是对称的，等等。 即使可以做到，通常也需要非常复杂的操作，非常不方便。 在本文中，基于多模态大语言模型，我们提出了 MLLM-SR，这是一种对话式符号回归方法，只需用自然语言指令描述要求，就可以生成符合要求的表达式。 通过对 Nguyen 数据集进行实验，我们可以证明 MLLM-SR 在拟合性能上领先于最先进的基线方法。 更重要的是，我们实验表明 MLLM-SR 能很好地理解我们添加到自然语言指令中的先验知识。 此外，添加先验知识可以有效地引导 MLLM-SR 生成正确的表达式。 显示英文摘要

摘要： Formulas are the language of communication between humans and nature. It is an important research topic of artificial intelligence to find expressions from observed data to reflect the relationship between each variable in the data, which is called a symbolic regression problem. The existing symbolic regression methods directly generate expressions according to the given observation data, and we cannot require the algorithm to generate expressions that meet specific requirements according to the known prior knowledge. For example, the expression needs to contain $\sin$ or be symmetric, and so on. Even if it can, it often requires very complex operations, which is very inconvenient. In this paper, based on multi-modal large language models, we propose MLLM-SR, a conversational symbolic regression method that can generate expressions that meet the requirements simply by describing the requirements with natural language instructions. By experimenting on the Nguyen dataset, we can demonstrate that MLLM-SR leads the state-of-the-art baselines in fitting performance. More notably, we experimentally demonstrate that MLLM-SR can well understand the prior knowledge we add to the natural language instructions. Moreover, the addition of prior knowledge can effectively guide MLLM-SR to generate correct expressions.