Vera Neplenbroek, Arianna Bisazza, Raquel Fernández
原文摘要
Generative large language models (LLMs) have been shown to exhibit harmful biases and stereotypes. While safety fine-tuning typically takes place in English, if at all, these models are being used by speakers of many different languages. There is existing evidence that the performance of these models is inconsistent across languages and that they discriminate based on demographic factors of the user. Motivated by this, we investigate whether the social stereotypes exhibited by LLMs differ as a function of the language used to prompt them, while controlling for cultural differences and task accuracy. To this end, we present MBBQ (Multilingual Bias Benchmark for Question-answering), a carefully curated version of the English BBQ dataset extended to Dutch, Spanish, and Turkish, which measures stereotypes commonly held across these languages. We further complement MBBQ with a parallel control dataset to measure task performance on the question-answering task independently of bias. Our results based on several open-source and proprietary LLMs confirm that some non-English languages suffer from bias more than English, even when controlling for cultural shifts. Moreover, we observe significant cross-lingual differences in bias behaviour for all except the most accurate models. With the release of MBBQ, we hope to encourage further research on bias in multilingual settings. The dataset and code are available at https://github.com/Veranep/MBBQ.
Adrian Sauter, Vera Neplenbroek, Georgios Vlassopoulos, Gianluigi Bardelloni
原文摘要
In subscription-based businesses, understanding why a customer intends to churn is as vital as the classification itself. We present a casestudy at a large European telecommunications provider, where we implement Text Bottleneck Models (TBMs) for post-call churn classifica-tion. The TBM distills dialogues into a sparse set of human-interpretable concepts and provides faithful, snippet-based evidence for everydecision. We show that the TBM performs competitively with black-box baselines and demonstrate potential business impact via automatedcall profiling and an interactive stakeholder dashboard. Our work demonstrates that the perceived trade-off between interpretability andpredictive performance can be bridged, providing the high-accuracy evidence needed for industrial retention strategies.
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表
Franziska Weeber University of Stuttgart, Universität Stuttgart and Humboldt Universität Berlin, Vera Neplenbroek, Jan Batzner Technical University of Munich and Weizenbaum Institute, Sebastian Padó
原文摘要
Personalization of LLMs by sociodemographic subgroup often improves user experience, but can also introduce or amplify biases and unfair outcomes across groups. Prior work has employed so-called personas, sociodemographic user attributes conveyed to a model, to study bias in LLMs by relying on a single cue to prompt a persona, such as user names or explicit attribute mentions. This disregards LLM sensitivity to prompt variation and the rarity of some cues in real interactions (external validity). We compare six commonly used persona cues across seven open and proprietary LLMs on four writing and advice tasks. While cues are overall highly correlated, they produce substantial variance in responses across personas that can change findings on persona-induced differences and bias. We therefore caution against claims based on single persona cues, especially when they are overly explicit and have low external validity.
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表