3 citations · 8 across the 7 of their papers we have counts for
8 papers
LLMs learn different forms of metacognition when trained to predict their own accuracy
Nicolas Yax, Stefano Palminteri, Pierre-Yves Oudeyer
Large language models are trained to always produce an answer, regardless of whether they possess the relevant knowledge, which leads them to fabricate facts. Prior work has shown…
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…
Large Language Models are Biased Reinforcement Learners
William M. Hayes, Nicolas Yax, Stefano Palminteri
In-context learning enables large language models (LLMs) to perform a variety of tasks, including learning to make reward-maximizing choices in simple bandit tasks. Given their pot…
PhyloLM : Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks
Nicolas Yax, Pierre-Yves Oudeyer, Stefano Palminteri
This paper introduces PhyloLM, a method adapting phylogenetic algorithms to Large Language Models (LLMs) to explore whether and how they relate to each other and to predict their p…
Relative Value Biases in Large Language Models
William M. Hayes, Nicolas Yax, Stefano Palminteri
Studies of reinforcement learning in humans and animals have demonstrated a preference for options that yielded relatively better outcomes in the past, even when those options are…