One paper by DFKI-NLP authors accepted to ACL 2025 Findings

One paper by Qianli Wang and Nils Feldhus has been accepted to the Findings Track at the 63rd Annual Meeting of the Association for Computational Linguistics 2025 (ACL 2025). In the paper, they introduce ZeroCF, a faithful approach for leveraging important words derived from feature attribution methods to generate counterfactual examples in a zero-shot setting. Second, they present a new framework, FitCF, which further verifies aforementioned counterfactuals by label flip verification and then inserts them as demonstrations for few-shot prompting.