3 papers
cs.CY2026
Who Gets Counted? Large Language Models, Informative Refusal, and Downstream Bias in Name-Based Demographic Classification
Christian Pipal, Morgan Wack, Eva-Maria Vogel
Researchers routinely infer gender, race, and ethnicity from names, yet the standard tools used for this task misclassify the groups that diversity research most needs to measure.…
cs.CV2026
What Does the Model See? Representation Validity in Multimodal LLM-Based Video Analysis
Christian Pipal, Eva-Maria Vogel, Morgan Wack
Multimodal large language models (MLLMs) allow social scientists to analyze video at scale, but researchers must decide which parts of a video to provide as input. We use the term…
cs.CL2026
Researchers waste 80% of LLM annotation costs by classifying one text at a time
Christian Pipal, Eva-Maria Vogel, Morgan Wack +1
Large language models (LLMs) are increasingly being used for text classification across the social sciences, yet researchers overwhelmingly classify one text per variable per promp…