bibtext: @inproceedings{
  le2024are,
  title={Are Language Models Robust Coreference Resolvers?},
  author={Nghia T. Le and Alan Ritter},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=MmBQSNHKUl}
  }
abstract: 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.
title: Are Language Models Robust Coreference Resolvers?
authors: Nghia T. Le Alan Ritter