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Found 4 papers, 1 papers with code
Large Language Models are Biased Reinforcement Learners
Given their potential use as (autonomous) decision-making agents, it is important to understand how these models perform such reinforcement learning (RL) tasks and the extent to which they are susceptible to biases.
PhyloLM : Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks
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 performance characteristics.
Relative Value Biases in Large Language Models
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 associated with lower absolute reward.
Studying and improving reasoning in humans and machines
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 study of (bounded) rationality.
