  Coreference resolution -- which is a crucial task for understanding discourse
and language at large -- has yet to witness widespread benefits from large
language models (LLMs). Moreover, coreference resolution systems largely rely
on supervised labels, which are highly expensive and difficult to annotate,
thus making it ripe for prompt engineering. In this paper, we introduce a
QA-based prompt-engineering method and discern \textit{generative}, pre-trained
LLMs' abilities and limitations toward the task of coreference resolution. Our
experiments show that GPT-2 and GPT-Neo can return valid answers, but that
their capabilities to identify coreferent mentions are limited and
prompt-sensitive, leading to inconsistent results.

Computation and Language;Machine Learning