Probing LLMs for Multilingual Discourse Generalization Through a Unified Label Set

Discourse understanding is essential for many NLP tasks, yet most existing work remains constrained by framework-dependent discourse representations.This work investigates whether large language models (LLMs) capture discourse knowledge that generalizes across languages and frameworks.We address this question along two dimensions: (1) developing a unified discourse relation label set to facilitate cross-lingual and cross-framework discourse analysis, and (2) probing LLMs to assess whether they encode generalizable discourse abstractions.Using multilingual discourse relation classification as a testbed, we examine a comprehensive set of 23 LLMs of varying sizes and multilingual capabilities.Our results show that LLMs, especially those with multilingual training corpora, can generalize discourse information across languages and frameworks.Further layer-wise analyses reveal that language generalization at the discourse level is most salient in the intermediate layers.Lastly, our error analysis provides an account of challenging relation classes.