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cs.CL2025

Measuring what Matters: Construct Validity in Large Language Model Benchmarks

Andrew M. Bean, Ryan Othniel Kearns, Angelika Romanou +39

Evaluating large language models (LLMs) is crucial for both assessing their capabilities and identifying safety or robustness issues prior to deployment. Reliably measuring abstrac…

cs.CL2025

Framing Migration: A Computational Analysis of UK Parliamentary Discourse

Vahid Ghafouri, Robert McNeil, Teodor Yankov +4

We present a large-scale computational analysis of migration-related discourse in UK parliamentary debates spanning over 75 years and compare it with US congressional discourse. Us…

cs.CL2025

Into the crossfire: evaluating the use of a language model to crowdsource gun violence reports

Adriano Belisario, Scott A. Hale, Luc Rocher

Gun violence is a pressing human rights issue that affects nearly every dimension of the social fabric, from healthcare and education to psychology and the economy. Reliable data o…

cs.CL2025

Training language models to be warm and empathetic makes them less reliable and more sycophantic

Lujain Ibrahim, Franziska Sofia Hafner, Luc Rocher

Artificial intelligence (AI) developers are increasingly building language models with warm and empathetic personas that millions of people now use for advice, therapy, and compani…

cs.CL2025

Gender Trouble in Language Models: An Empirical Audit Guided by Gender Performativity Theory

Franziska Sofia Hafner, Ana Valdivia, Luc Rocher

Language models encode and subsequently perpetuate harmful gendered stereotypes. Research has succeeded in mitigating some of these harms, e.g. by dissociating non-gendered terms s…