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 associated with lower absolute reward. The present
study tested whether large language models would exhibit a similar bias. We had
gpt-4-1106-preview (GPT-4 Turbo) and Llama-2-70B make repeated choices between
pairs of options with the goal of maximizing payoffs. A complete record of
previous outcomes was included in each prompt. Both models exhibited relative
value decision biases similar to those observed in humans and animals. Making
relative comparisons among outcomes more explicit magnified the bias, whereas
prompting the models to estimate expected outcomes caused the bias to
disappear. These results have implications for the potential mechanisms that
contribute to context-dependent choice in human agents.