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Found 4 papers, 1 papers with code
Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks
This paper introduces PhyloLM, a method applying phylogenetic algorithms to Large Language Models to explore their finetuning relationships, and predict their performance characteristics.
Modelling crypto markets by multi-agent reinforcement learning
Building on a previous foundation work (Lussange et al. 2020), this study introduces a multi-agent reinforcement learning (MARL) model simulating crypto markets, which is calibrated to the Binance's daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 2022.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
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.
