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Bye Bye Perspective API: Lessons for Measurement Infrastructure in NLP, CSS and LLM Evaluation
David Hartmann, Manuel Tonneau, Angelie Kraft +7
The closure of Perspective API at the end of 2026 discards what has functioned as the de facto standard for automated toxicity measurement in NLP, CSS, and LLM evaluation research.…
Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Bias
Manuel Tonneau, Neil K. R. Seghal, Niyati Malhotra +7
Demographic cue-based evaluation is widely used to study how large language models (LLMs) adapt their responses to signaled demographic attributes within and across groups. This ap…
When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits
Jabez Magomere, Emanuele La Malfa, Manuel Tonneau +2
Online misinformation remains a critical challenge, and fact-checkers increasingly rely on claim matching systems that use sentence embedding models to retrieve relevant fact-check…
HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter
Manuel Tonneau, Diyi Liu, Niyati Malhotra +4
To address the global challenge of online hate speech, prior research has developed detection models to flag such content on social media. However, due to systematic biases in eval…
From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets
Manuel Tonneau, Diyi Liu, Samuel Fraiberger +3
Perceptions of hate can vary greatly across cultural contexts. Hate speech (HS) datasets, however, have traditionally been developed by language. This hides potential cultural bias…
Indian-BhED: A Dataset for Measuring India-Centric Biases in Large Language Models
Khyati Khandelwal, Manuel Tonneau, Andrew M. Bean +2
Large Language Models (LLMs), now used daily by millions, can encode societal biases, exposing their users to representational harms. A large body of scholarship on LLM bias exists…