[lukev]

Ok but I have to point out something important here. Presumably, the model you're talking about was trained on chemical/drug inputs. So it models a space of chemical interactions, which means insights could be plausible.

GPT-5 (and other LLMs) are by definition language models and though they will happily spew tokens about whatever you ask, they don't necessarily have the training data to properly encode the latent space of (e.g) drug interactions.

Confusing these two concepts could be deadly.

[Difwif]

Seems short sighted to me. LLMs could have any data in their training set encoded as tokens. Either new specialized tokens are explicitly included (e.g: Vision models) or the language encoded version of everything that usually exists (e.g: the research paper and the csv with the data).

To improve next token prediction performance on these datasets and generalize requires a much richer latent space. I think it could theoretically lead to better results from cross-domain connections (ex: being fluent in a specific area of advanced mathematics, quantum mechanics, and materials engineering is key to a particular breakthrough)

[anthk]

Then you are now implementing a parser.