@inproceedings{metropolitansky2025towards,
author = {Metropolitansky, Dasha and Larson, Jonathan},
title = {Towards Effective Extraction and Evaluation of Factual Claims},
booktitle = {ACL 2025 Main Conference},
year = {2025},
month = {February},
abstract = {A common strategy for fact-checking long-form content generated by Large Language Models (LLMs) is extracting simple claims that can be verified independently. Since inaccurate or incomplete claims compromise fact-checking results, ensuring claim quality is critical. However, the lack of a standardized evaluation framework impedes assessment and comparison of claim extraction methods. To address this gap, we propose a framework for evaluating claim extraction in the context of fact-checking along with automated, scalable, and replicable methods for applying this framework, including novel approaches for measuring coverage and decontextualization. We also introduce Claimify, an LLM-based claim extraction method, and demonstrate that it outperforms existing methods under our evaluation framework. A key feature of Claimify is its ability to handle ambiguity and extract claims only when there is high confidence in the correct interpretation of the source text.},
url = {http://approjects.co.za/?big=en-us/research/publication/towards-effective-extraction-and-evaluation-of-factual-claims/},
}