In our previous four posts we've argued that LLM AIs should not be in the driver's seat of ordinary meaning inquiries. In so stating, we don't deny that AI tools have certain advantages over most current corpus tools: Their front-end interface is more intuitive to use and they can process data faster than human coders.
These are two-edged swords for reasons we discussed yesterday. Without further refinements, the user-friendliness of the interface and speed of the outputs could cut against the utility of LLM AIs in the empirical inquiry into ordinary meaning—by luring the user into thinking that a sensible-sounding answer generated by an accessible, tech-driven tool must be rooted in empiricism.
That said, we see two means of leveraging LLM AIs' advantages while minimizing these risks. One is for linguists to learn from the AI world and leverage the above advantages into the tools of corpus linguistics. Another is for LLM AIs to learn from corpus linguists by building tools that open the door to truly empirical analysis of ordinary language.
