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The probing classifiers framework has been employed for interpreting deepneural network models for a variety of natural language processing (NLP)applications. Studies, however, have largely focused on sentencelevel NLPtasks. This work is the first to apply the probing paradigm to representationslearned for document-level information extraction (IE). We designed eightembedding probes to analyze surface, semantic, and event-understandingcapabilities relevant to document-level event extraction. We apply them to therepresentations acquired by learning models from three different LLM-baseddocument-level IE approaches on a standard dataset. We found that trainedencoders from these models yield embeddings that can modestly improve argumentdetections and labeling but only slightly enhance event-level tasks, albeittrade-offs in information helpful for coherence and event-type prediction. Wefurther found that encoder models struggle with document length andcross-sentence discourse.

Probing Representations for Document-level Event Extraction