| Title: |
Assessing the Capabilities of Large Language Models in Coreference: An Evaluation |
| Authors: |
Gan, Yujian; Yu, Juntao; Poesio, Massimo; Sub Natural Language Processing; Natural Language Processing; Calzolari, Nicoletta; Kan, Min-Yen; Hoste, Veronique; Lenci, Alessandro; Sakti, Sakriani; Xue, Nianwen |
| Publication Year: |
2024 |
| Subject Terms: |
Coreference; Large Language Models; Prompt Engineering; Theoretical Computer Science; Computational Theory and Mathematics; Computer Science Applications |
| Description: |
This paper offers a nuanced examination of the role Large Language Models (LLMs) play in coreference resolution, aimed at guiding the future direction in the era of LLMs. We carried out both manual and automatic analyses of different LLMs' abilities, employing different prompts to examine the performance of different LLMs, obtaining a comprehensive view of their strengths and weaknesses. We found that LLMs show exceptional ability in understanding coreference. However, harnessing this ability to achieve state of the art results on traditional datasets and benchmarks isn't straightforward. Given these findings, we propose that future efforts should: (1) Improve the scope, data, and evaluation methods of traditional coreference research to adapt to the development of LLMs. (2) Enhance the fine-grained language understanding capabilities of LLMs. |
| Document Type: |
book part |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://dspace.library.uu.nl/handle/1874/482450 |
| Availability: |
https://dspace.library.uu.nl/handle/1874/482450 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.D94EB3B1 |
| Database: |
BASE |