34 citations · 103 across the 67 of their papers we have counts for
16 papers · 1 filter
Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics Statements
Antonia Karamolegkou, Sandrine Schiller Hansen, Ariadni Christopoulou +3
What ethical concerns, if any, do LLM researchers have? We introduce EthiCon, a corpus of 1,580 ethical concern statements extracted from scientific papers published in the ACL Ant…
Multi3Hate: Multimodal, Multilingual, and Multicultural Hate Speech Detection with Vision-Language Models
Minh Duc Bui, Katharina von der Wense, Anne Lauscher
Warning: this paper contains content that may be offensive or upsetting Hate speech moderation on global platforms poses unique challenges due to the multimodal and multilingual na…
Local Contrastive Editing of Gender Stereotypes
Marlene Lutz, Rochelle Choenni, Markus Strohmaier +1
Stereotypical bias encoded in language models (LMs) poses a threat to safe language technology, yet our understanding of how bias manifests in the parameters of LMs remains incompl…
SoK: Towards Security and Safety of Edge AI
Tatjana Wingarz, Anne Lauscher, Janick Edinger +3
Advanced AI applications have become increasingly available to a broad audience, e.g., as centrally managed large language models (LLMs). Such centralization is both a risk and a p…
WinoPron: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical Case
Vagrant Gautam, Julius Steuer, Eileen Bingert +3
While measuring bias and robustness in coreference resolution are important goals, such measurements are only as good as the tools we use to measure them. Winogender Schemas (Rudin…
The Lou Dataset -- Exploring the Impact of Gender-Fair Language in German Text Classification
Andreas Waldis, Joel Birrer, Anne Lauscher +1
Gender-fair language, an evolving German linguistic variation, fosters inclusion by addressing all genders or using neutral forms. Nevertheless, there is a significant lack of reso…
Decoding Multilingual Moral Preferences: Unveiling LLM's Biases Through the Moral Machine Experiment
Karina Vida, Fabian Damken, Anne Lauscher
Large language models (LLMs) increasingly find their way into the most diverse areas of our everyday lives. They indirectly influence people's decisions or opinions through their d…
Why do LLaVA Vision-Language Models Reply to Images in English?
Musashi Hinck, Carolin Holtermann, Matthew Lyle Olson +6
We uncover a surprising multilingual bias occurring in a popular class of multimodal vision-language models (VLMs). Including an image in the query to a LLaVA-style VLM significant…
The Echoes of Multilinguality: Tracing Cultural Value Shifts during LM Fine-tuning
Rochelle Choenni, Anne Lauscher, Ekaterina Shutova
Texts written in different languages reflect different culturally-dependent beliefs of their writers. Thus, we expect multilingual LMs (MLMs), that are jointly trained on a concate…
Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language
Jan Kaiser, Annika Eichler, Anne Lauscher
Autonomous tuning of particle accelerators is an active and challenging field of research with the goal of enabling novel accelerator technologies cutting-edge high-impact applicat…
What Can Natural Language Processing Do for Peer Review?
Ilia Kuznetsov, Osama Mohammed Afzal, Koen Dercksen +21
The number of scientific articles produced every year is growing rapidly. Providing quality control over them is crucial for scientists and, ultimately, for the public good. In mod…
Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLP
Vagrant Gautam, Arjun Subramonian, Anne Lauscher +1
Personal names simultaneously differentiate individuals and categorize them in ways that are important in a given society. While the natural language processing community has thus…
Robust Pronoun Fidelity with English LLMs: Are they Reasoning, Repeating, or Just Biased?
Vagrant Gautam, Eileen Bingert, Dawei Zhu +2
Robust, faithful and harm-free pronoun use for individuals is an important goal for language model development as their use increases, but prior work tends to study only one or two…
Argument Quality Assessment in the Age of Instruction-Following Large Language Models
Henning Wachsmuth, Gabriella Lapesa, Elena Cabrio +5
The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, wri…
Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQ
Carolin Holtermann, Paul Röttger, Timm Dill +1
Large language models (LLMs) need to serve everyone, including a global majority of non-English speakers. However, most LLMs today, and open LLMs in particular, are often intended…
What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition
Carolin Holtermann, Markus Frohmann, Navid Rekabsaz +1
The knowledge encapsulated in a model is the core factor determining its final performance on downstream tasks. Much research in NLP has focused on efficient methods for storing an…