Do language models capture implied discourse meanings? An investigation with exhaustivity implicatures of Korean morphology
This addresses the problem of evaluating language models' ability to handle non-literal discourse features for researchers in computational linguistics, but it is incremental as it builds on prior work on semantic feature recovery.
The study investigated whether large language models can capture implied discourse meanings, specifically exhaustivity implicatures associated with Korean Differential Object Marking, and found that discourse meanings of grammatical markers are more challenging to encode than those of discourse markers.
Markedness in natural language is often associated with non-literal meanings in discourse. Differential Object Marking (DOM) in Korean is one instance of this phenomenon, where post-positional markers are selected based on both the semantic features of the noun phrases and the discourse features that are orthogonal to the semantic features. Previous work has shown that distributional models of language recover certain semantic features of words -- do these models capture implied discourse-level meanings as well? We evaluate whether a set of large language models are capable of associating discourse meanings with different object markings in Korean. Results suggest that discourse meanings of a grammatical marker can be more challenging to encode than that of a discourse marker.