  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.
