Surprising Efficacy of Fine-Tuned Transformers for Fact-Checking over Larger Language Models.
| Anthology ID: | DBLP:conf/sigir/Setty24a |
|---|---|
| Volume: | Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024 |
| Year: | 2024 |
| Venue: | Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) |
| Publisher: | ACM |
| Pages: | 2842-2846 |
| URL: | https://doi.org/10.1145/3626772.3661361 |
| DOI: | https://doi.org/10.1145/3626772.3661361 |
| DBLP: | conf/sigir/Setty24a |
| BibTeX: |
@inproceedings{setty-2024-surprising,
author = {Vinay Setty},
editor = {Grace Hui Yang and
Hongning Wang and
Sam Han and
Claudia Hauff and
Guido Zuccon and
Yi Zhang},
title = {{Surprising Efficacy of Fine-Tuned Transformers for Fact-Checking over Larger Language Models}},
booktitle = {{Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024}},
pages = {2842--2846},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3626772.3661361},
doi = {https://doi.org/10.1145/3626772.3661361}
}