Text adventures are hard because you have structured data (eg. a scene with N objects in it), but you want to generate a text fragment dynamically as the state of those objects changes.

It meaningless to just randomly generate text fragments; you have to parse the input of free text (hard, eg. turn off the light; yes it is hard when its free text), update the state of the scene (easy, eg. set light level to low)...

... and then return a new text snippet that describes the scene (extremely hard, unless you constrain the state transitions to a finite hardcoded set).

so, like, tldr, you could do something really cool here, to use a language model to describe a scene over a continuous range of states using a finite set of samples and that would be amazing!

...but, I dont really see seeding a text fragment from a set of words being a particularly interesting addition to the field.