Surprising Efficacy of Fine-Tuned Transformers for Fact-Checking over Larger Language Models.

Vinay Setty


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} }