Concept Generalization in Humans and Large Language Models: Insights from the Number Game

Fuente: arXiv
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Auteurs principaux: Bazigaran, Arghavan, Sohn, Hansem
Format: Preprint
Publié: 2025
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author Bazigaran, Arghavan
Sohn, Hansem
author_facet Bazigaran, Arghavan
Sohn, Hansem
contents We compare human and large language model (LLM) generalization in the number game, a concept inference task. Using a Bayesian model as an analytical framework, we examined the inductive biases and inference strategies of humans and LLMs. The Bayesian model captured human behavior better than LLMs in that humans flexibly infer rule-based and similarity-based concepts, whereas LLMs rely more on mathematical rules. Humans also demonstrated a few-shot generalization, even from a single example, while LLMs required more samples to generalize. These contrasts highlight the fundamental differences in how humans and LLMs infer and generalize mathematical concepts.
format Preprint
id arxiv_https___arxiv_org_abs_2512_20162
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Concept Generalization in Humans and Large Language Models: Insights from the Number Game
Bazigaran, Arghavan
Sohn, Hansem
Artificial Intelligence
We compare human and large language model (LLM) generalization in the number game, a concept inference task. Using a Bayesian model as an analytical framework, we examined the inductive biases and inference strategies of humans and LLMs. The Bayesian model captured human behavior better than LLMs in that humans flexibly infer rule-based and similarity-based concepts, whereas LLMs rely more on mathematical rules. Humans also demonstrated a few-shot generalization, even from a single example, while LLMs required more samples to generalize. These contrasts highlight the fundamental differences in how humans and LLMs infer and generalize mathematical concepts.
title Concept Generalization in Humans and Large Language Models: Insights from the Number Game
topic Artificial Intelligence
url https://arxiv.org/abs/2512.20162