Code for the paper "Enhancing FEVER-Style Claim Fact-Checking Against Wikipedia: A Diagnostic Taxonomy and a Generative Framework"
@inproceedings{10.1007/978-3-031-88708-6_20,
author = {Chernyavskiy, Anton and Ilvovsky, Dmitry and Nakov, Preslav},
title = {Enhancing FEVER-Style Claim Fact-Checking Against Wikipedia: A Diagnostic Taxonomy and a Generative Framework},
year = {2025},
isbn = {978-3-031-88707-9},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-88708-6_20},
doi = {10.1007/978-3-031-88708-6_20},
abstract = {Fact-checking is a crucial yet challenging task that continues to gain importance. In an effort to address this issue, the FEVER large-scale dataset was developed to facilitate evidence-based fact-checking using Wikipedia as a reference. Despite numerous proposed approaches and evaluations on this dataset, a comprehensive understanding of the errors made by these approaches is still lacking. Here, we aim to bridge this gap. We introduce a diagnostic taxonomy and a generative framework to enhance FEVER-style fact-checking. We establish a taxonomy of errors and we construct a diagnostic dataset that enables the analysis of the errors made by state-of-the-art models as well as their distribution within the FEVER dataset. Additionally, we provide a set of prompts to generate examples within this taxonomy. Our experiments demonstrate promising results through the utilization of these generated examples for fine-tuning.},
booktitle = {Advances in Information Retrieval: 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6–10, 2025, Proceedings, Part I},
pages = {310–325},
numpages = {16},
keywords = {Fact-checking, Diagnostic Taxonomy, Diagnostic Dataset, Synthetic Data, Natural Language Inference, Wikipedia},
location = {Lucca, Italy}
}