I'll talk about three major tensions in NLP resulting from rapid advances of large language models.  First, we are in the middle of a switch from vertical research on tasks (parsing, coreference, sentiment) to the kind of horizontal tech stacks that exist elsewhere in CS.  Second, there is a fundamental tension between the factors that drive machine learning (scaled, end-to-end optimization of monoliths) and the factors that drive human software engineering (modularity, abstraction, interoperability).  Third, modern models can be stunning on some axes while showing major gaps on others -- they can, in different ways, simultaneously be general, fragile, or dangerous.  I'll give an NLP perspective on these issues along with some possible solution directions.

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