Papers by Vera Schmitt
Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem (2025.coling-main)
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| Challenge: | Natural language explanations (NLEs) are vital for elucidating the reasoning behind large language model (LLM) decisions. |
| Approach: | They propose a role-modeling approach that employs two LLMs as generator and critic to generate and refine NLEs. |
| Outcome: | The proposed model outperforms self-refine and can perform with less powerful LLMs. |
Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation (2026.acl-long)
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Qianli Wang, Van Bach Nguyen, Yihong Liu, Fedor Splitt, Nils Feldhus, Christin Seifert, Hinrich Schuetze, Sebastian Möller, Vera Schmitt
| Challenge: | Large language models excel at generating English counterfactuals but their effectiveness in generating multilingual counterfacts remains unclear. |
| Approach: | They conduct automatic evaluations on both directly generated and derived counterfactuals in six languages and find that cross-lingual perturbations follow common strategic principles. |
| Outcome: | The proposed models show that translation-based counterfactuals offer higher validity than their directly generated counterparts, but still fall short of matching the quality of the original English counterf actuals. |
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation (2025.findings-acl)
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Qianli Wang, Nils Feldhus, Simon Ostermann, Luis Felipe Villa-Arenas, Sebastian Möller, Vera Schmitt
| Challenge: | Existing frameworks for counterfactual examples are lacking for many tasks. |
| Approach: | They propose a faithful approach for leveraging important words from feature attribution methods to generate counterfactual examples in a zero-shot setting. |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks on many tasks. |
Fine-tuning with Hierarchical Prompting for Robust Propaganda Classification Across Annotation Schemas (2026.findings-acl)
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Lukas Stähelin, Veronika Solopova, Max Upravitelev, David Kaplan, Premtim Sahitaj, Ariana Sahitaj, Charlott Jakob, Sebastian Möller, Vera Schmitt
| Challenge: | Propaganda detection in social media is challenging due to noisy, short texts and low annotation agreements. |
| Approach: | They propose a new intent-focused taxonomy of propaganda techniques and compare it against an established, higher-agreement schema. |
| Outcome: | The proposed taxonomy outperforms existing models and reveals methodological differences hidden in base models. |
Uncovering Temporal Framing in the News (2026.acl-long)
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Tarek Mahmoud, Veronika Solopova, Premtim Sahitaj, Ariana Sahitaj, Max Upravitelev, Mervat Abassy, Hana Fatima Shaikh, Neda Foroutan, Vera Schmitt, Preslav Nakov
| Challenge: | Temporal language is used to structure meaning rather than report chronology in news discourse . a recent study focused on temporal expression extraction and temporal reasoning . |
| Approach: | They propose a taxonomy of eight temporal frames grounded in prior work on time and framing . they analyze frame prevalence, co-occurrence patterns, and lexical cues from a news corpus . |
| Outcome: | The proposed taxonomy outperforms zero-shot models at the sentence level . it shows that temporal framing is learnable at the sentences level compared to other methods . |
PolBiX: Detecting LLMs’ Political Bias in Fact-Checking through X-phemisms (2025.findings-emnlp)
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| Challenge: | a few models show tendencies of political bias, but this is not mitigated by explicitly calling for objectivism in prompts. |
| Approach: | They investigate political bias by exchanging words with euphemisms or dysphemismas in German claims. |
| Outcome: | The proposed model shows that political bias influences truthfulness assessment more than political leaning . |
Persona Prompting as a Lens on LLM Social Reasoning (2026.eacl-long)
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Jing Yang, Moritz Hechtbauer, Elisabeth Khalilov, Evelyn Luise Brinkmann, Vera Schmitt, Nils Feldhus
| Challenge: | Persona prompting (PP) is increasingly used to steer large language models towards user-specific generation, but its effect on rationales remains underexplored. |
| Approach: | They examine how LLM-generated rationales vary when conditioned on different demographic personas . they use word-level rationale annotations to measure agreement with human annotations based on PP . |
| Outcome: | The proposed model improves classification on the most subjective task, but fails to align with real-world demographic counterparts. |
From Weights to Activations: Is Steering the Next Frontier of Adaptation? (2026.acl-long)
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Simon Ostermann, Daniil Gurgurov, Tanja Baeumel, Michael A. Hedderich, Sebastian Lapuschkin, Wojciech Samek, Vera Schmitt
| Challenge: | Pre-trained large language models are the basis of a wide range of NLP tasks. |
| Approach: | They propose to use parameter updates and parameter-efficient adaptation to modify behavior of large language models. |
| Outcome: | The proposed method enables local and reversible behavioral change without parameter updates. |
Multilingual Datasets for Custom Input Extraction and Explanation Requests Parsing in Conversational XAI Systems (2025.findings-emnlp)
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Qianli Wang, Tatiana Anikina, Nils Feldhus, Simon Ostermann, Fedor Splitt, Jiaao Li, Yoana Tsoneva, Sebastian Möller, Vera Schmitt
| Challenge: | Current ConvXAI systems are based on intent recognition to accurately identify the user’s desired intention and map it to an explainability method. |
| Approach: | They propose a multilingual extension of the CoXQL dataset spanning five typologically diverse languages, including one low-resource language. |
| Outcome: | The proposed model enables multilingual generalization in a multilingual dataset spanning five typologically diverse languages, including one low-resource language. |