10 citations · 17 across the 17 of their papers we have counts for
6 papers · 1 filter
WildSEEK: Evaluating Language Models for Information-Seeking
Tanise Ceron, Joachim Baumann, Elisa Bassignana +3
Language models are increasingly mediating information access to end users, urging a systematic evaluation of their responses for a fair and reliable information ecosystem. Existin…
Humanly: A Configurable and Traceable Environment for Human-AI Collaborative Writing
Shenzhe Zhu, Haoqian Zhang, Xu Yang +7
Teachers, conference chairs, and public readers all judge writing from limited evidence, seeing only a finished document and not the process that produced it. Final text alone cann…
Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability
Alicia Parrish, Rajat Shinde, Sanket Badhe +57
Current AI safety evaluation and benchmarking frameworks predominantly rely on Western-centric culture-agnostic defaults that mask critical regional laws, socio-linguistic nuances,…
Auditing Google's AI Overviews and Featured Snippets: A Case Study on Baby Care and Pregnancy
Desheng Hu, Joachim Baumann, Aleksandra Urman +4
Google Search increasingly surfaces AI-generated content through features like AI Overviews (AIO) and Featured Snippets (FS), which users frequently rely on despite having no contr…
Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation
Joachim Baumann, Paul Röttger, Aleksandra Urman +4
Large language models are rapidly transforming social science research by enabling the automation of labor-intensive tasks like data annotation and text analysis. However, LLM outp…
SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors
Tiancheng Hu, Joachim Baumann, Lorenzo Lupo +3
Large language model (LLM) simulations of human behavior have the potential to revolutionize the social and behavioral sciences, if and only if they faithfully reflect real human b…