  Recent work on extending coreference resolution across domains and languages
relies on annotated data in both the target domain and language. At the same
time, pre-trained large language models (LMs) have been reported to exhibit
strong zero- and few-shot learning abilities across a wide range of NLP tasks.
However, prior work mostly studied this ability using artificial sentence-level
datasets such as the Winograd Schema Challenge. In this paper, we assess the
feasibility of prompt-based coreference resolution by evaluating
instruction-tuned language models on difficult, linguistically-complex
coreference benchmarks (e.g., CoNLL-2012). We show that prompting for
coreference can outperform current unsupervised coreference systems, although
this approach appears to be reliant on high-quality mention detectors. Further
investigations reveal that instruction-tuned LMs generalize surprisingly well
across domains, languages, and time periods; yet continued fine-tuning of
neural models should still be preferred if small amounts of annotated examples
are available.
