3 citations · 6 across the 5 of their papers we have counts for
6 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…
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…
Studying and improving reasoning in humans and machines
Nicolas Yax, Hernan Anlló, Stefano Palminteri
In the present study, we investigate and compare reasoning in large language models (LLM) and humans using a selection of cognitive psychology tools traditionally dedicated to the…