Text2Model: Generating dynamic chemical reactor models using large language models (LLMs)
This work addresses a domain-specific task for chemical engineers by automating model generation, but it is incremental as it builds on existing LLM fine-tuning methods.
The paper tackles the problem of generating dynamic chemical reactor models from textual descriptions by fine-tuning Llama 3.1 8B Instruct on synthetic Modelica code, resulting in improved syntactic and semantic accuracy compared to baseline models, though it lacks generalization to unseen scenarios.
As large language models have shown remarkable capabilities in conversing via natural language, the question arises as to how LLMs could potentially assist chemical engineers in research and industry with domain-specific tasks. We generate dynamic chemical reactor models in Modelica code format from textual descriptions as user input. We fine-tune Llama 3.1 8B Instruct on synthetically generated Modelica code for different reactor scenarios. We compare the performance of our fine-tuned model to the baseline Llama 3.1 8B Instruct model and GPT4o. We manually assess the models' predictions regarding the syntactic and semantic accuracy of the generated dynamic models. We find that considerable improvements are achieved by the fine-tuned model with respect to both the semantic and the syntactic accuracy of the Modelica models. However, the fine-tuned model lacks a satisfactory ability to generalize to unseen scenarios compared to GPT4o.