4 papers
Post-training makes large language models less human-like
Marcel Binz, Elif Akata, Abdullah Almaatouq +76
Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, w…
StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs
Pierre Le Jeune, Étienne Duchesne, Weixuan Xiao +4
Multilingual studies of social bias in open-ended LLM generation remain limited: most existing benchmarks are English-centric, template-based, or restricted to recognizing pre-spec…
The Moral Turing Test: Evaluating Human-LLM Alignment in Moral Decision-Making
Basile Garcia, Crystal Qian, Stefano Palminteri
As large language models (LLMs) become increasingly integrated into society, their alignment with human morals is crucial. To better understand this alignment, we created a large c…
LogProber: Disentangling confidence from contamination in LLM responses
Nicolas Yax, Pierre-Yves Oudeyer, Stefano Palminteri
In machine learning, contamination refers to situations where testing data leak into the training set. The issue is particularly relevant for the evaluation of the performance of L…