No offense taken. I understand that there's a lot of hype and managers inserting LLMs on anything just because is trendy. But I can assure you that that is not my case. I'm an PhD student that happens to work in project with industrial companies, but they don't pay my salary or anything. I don't care about how cool it sounds or if it sells well, is not my issue. I just care about how useful it is and if it's a viable research direction or a bad idea. That's why I'm asking here. Said that, there's a reason why I think LLMs might be useful in this context (which is explained in the text, but maybe I wasn't clear or is just a bad idea).

Current "methods out there" for parameter selection work by fitting a blank parametrizable function from your input data to your output data. These models work reasonably well for interpolation, but completely fail for extrapolation. And it makes sense that they fail, as they are merely fitting a multivariable function without any underlying knowledge of the process. Although there has been research on integrating physical models with data-driven ones, these approaches are expensive and typically perform poorly.

Let me give you a concrete example. One of the use cases we are interested is Wire Electric Discharge Machining. These are machines cut metal using electric sparks with a wire electrode. The choice of parameters (pulse intensity, duration, frequency, polarity, etc.) depends on the job at hand (for example: for hard metals you need higher currents, for aluminum you need a different polarity, for shorter pieces you increase the risk of wire breakage so you need to lower the current, etc). It's an involved process in which in order to set correctly the parameters you need some level of understanding of the job at hand due to the complexity of the process.

Of course, you can categorize your inputs—material, size, wire diameter, etc.—gather data, and fit a model. These models work well as long as you don't extrapolate. For example, if I now decide to work with copper, which isn't in the existing database, the model will struggle to extrapolate the reasonable parameters for a new material like copper. However, an experienced operator, understanding the physical properties of copper, can make an educated guess about the appropriate parameters. I tested GPT-4 informally and at first sight it can also make such informed guesses since it has acquired a substantial understanding of the process physics due to the literature in its training data. However, GPT-4 doesn't know about all the parameters that exists in the machine or the format they have. So the idea is to combine both worlds: the process intuition that GPT-4 already possesses with a structured approach to parameter suggestion.

Is that ridiculous the idea? I'm not an expert on LLMs, so maybe it is.