  In NLP, Event Coreference Resolution (ECR) is the task of connecting event
clusters that refer to the same underlying real-life event, usually via neural
systems. In this work, we investigate using abductive free-text rationales
(FTRs) generated by modern autoregressive LLMs as distant supervision of
smaller student models for cross-document coreference (CDCR) of events. We
implement novel rationale-oriented event clustering and knowledge distillation
methods for event coreference scoring that leverage enriched information from
the FTRs for improved CDCR without additional annotation or expensive document
clustering. Our model using coreference specific knowledge distillation
achieves SOTA B3 F1 on the ECB+ and GVC corpora and we establish a new baseline
on the AIDA Phase 1 corpus. Our code can be found at
https://github.com/csu-signal/llama_cdcr
