In addition, the large language model performs controlled molecular generation by controlling the levels of similarity to the given parent molecules, based on simple descriptive prompts. Moreover, the large language model successfully performs guided molecular generation, as shown by its ability to effectively manipulate the electronic structure of molecules using simple, natural-language prompts. This was demonstrated in the cases of electronwithdrawing group (EWG) and electron-donating group (EDG) prompts, where the model successfully lowered and raised the HOMO energy of the generated molecules relative to the parent molecules, compared to prompts that did not explicitly mention electronic structure changes. These results showcase the model's capacity to understand and respond to specific electronic structure-related instructions, enabling targeted control over the properties of the generated molecules.