[gattilorenz]

I think so. First of all, knowing some linguistics will teach you terms and concepts (e.g. parse tree, phrase, morpheme, phoneme, etc) that will both help you find relevant literature and avoid reinventing terms for stuff that is widely known (so others will more readily find your work).

Language models are currently the best solution for many problems, but it's hard to predict how we will move forward from here. Maybe the inclusion of linguistic information, or linguistic-inspired knowledge, or whatever, will be the key to having better results, or saving training time/resources. With no linguistics background, I imagine it's hard to get ideas going in that direction (and test if it's actually a good direction)

[mothcamp]

I agree. I think having linguistics knowledge can help especially in applied situations. Linguistics knowledge can help create fallback systems when an ML system fails, or help build rules to amplify or dampen the confidence of a response from an ML system, or aid in the engineering of a system (all that comes before or after the ML blackbox).

Sort of like an algorithmic trader knowing market microstructure intimately (versus only pure statistics).