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

• URL: http://arxiv.org/abs/2512.20162v1
• Date: Tue, 23 Dec 2025 08:41:03 GMT
• Title: Concept Generalization in Humans and Large Language Models: Insights from the Number Game
• Abstract summary: We compare human and large language model (LLM) generalization in the number game.<n>We examined the inductive biases and inference strategies of humans and LLMs.
• Score: 0.09549044630322329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Presupposition and Reasoning in Conditionals: A Theory-Based Study of Humans and LLMs [1.0599607477285324]
  We compare human judgments and LLM predictions on a normed dataset of conditional sentences.<n>Results show that humans integrate probabilistic and pragmatic cues in their judgment.<n>We observe models that best match human ratings often lack coherent pragmatic reasoning.
  arXiv  Detail & Related papers  (2026-05-18T13:08:20Z)
• Blind to the Human Touch: Overlap Bias in LLM-Based Summary Evaluation [89.52571224447111]
  Large language model (LLM) judges have often been used alongside traditional, algorithm-based metrics for tasks like summarization.<n>We provide an LLM judge bias analysis as a function of overlap with human-written responses in the domain of summarization.
  arXiv  Detail & Related papers  (2026-02-07T19:39:28Z)
• From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning [63.25540801694765]
  Large Language Models (LLMs) demonstrate striking linguistic abilities, yet whether they achieve this same balance remains unclear.<n>We apply the Information Bottleneck principle to quantitatively compare how LLMs and humans navigate this compression-meaning trade-off.
  arXiv  Detail & Related papers  (2025-05-21T16:29:00Z)
• Leveraging Human Production-Interpretation Asymmetries to Test LLM Cognitive Plausibility [7.183662547358301]
  We examine whether large language models process language similarly to humans.<n>We find that some LLMs do quantitatively and qualitatively reflect human-like asymmetries between production and interpretation.
  arXiv  Detail & Related papers  (2025-03-21T23:25:42Z)
• Non-literal Understanding of Number Words by Language Models [33.24263583093367]
  Humans naturally interpret numbers non-literally, combining context, world knowledge, and speaker intent.<n>We investigate whether large language models (LLMs) interpret numbers similarly, focusing on hyperbole and pragmatic halo effects.
  arXiv  Detail & Related papers  (2025-02-10T07:03:00Z)
• Large Language Models Reflect the Ideology of their Creators [71.65505524599888]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language.<n>This paper shows that the ideological stance of an LLM appears to reflect the worldview of its creators.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• Can DPO Learn Diverse Human Values? A Theoretical Scaling Law [7.374590753074647]
  Preference learning trains models to distinguish between preferred and non-preferred responses based on human feedback.<n>This paper introduces a new theoretical framework to analyze how generalization scales with value diversity and sample quantity.<n>Our framework rigorously assesses how well models generalize after a finite number of gradient steps.
  arXiv  Detail & Related papers  (2024-08-06T22:11:00Z)
• A Survey on Human Preference Learning for Large Language Models [81.41868485811625]
  The recent surge of versatile large language models (LLMs) largely depends on aligning increasingly capable foundation models with human intentions by preference learning. This survey covers the sources and formats of preference feedback, the modeling and usage of preference signals, as well as the evaluation of the aligned LLMs.
  arXiv  Detail & Related papers  (2024-06-17T03:52:51Z)
• Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function [3.7078759896522953]
  We evaluate large language models (LLMs) for their diversity of uses. We consider a setting where these deployment decisions are made by people. We collect a dataset of 19K examples of how humans make generalizations across 79 tasks from the MMLU and BIG-Bench benchmarks.
  arXiv  Detail & Related papers  (2024-06-03T14:45:21Z)
• A Theory of Response Sampling in LLMs: Part Descriptive and Part Prescriptive [53.08398658452411]
  Large Language Models (LLMs) are increasingly utilized in autonomous decision-making.<n>We show that this sampling behavior resembles that of human decision-making.<n>We show that this deviation of a sample from the statistical norm towards a prescriptive component consistently appears in concepts across diverse real-world domains.
  arXiv  Detail & Related papers  (2024-02-16T18:28:43Z)
• Towards Concept-Aware Large Language Models [56.48016300758356]
  Concepts play a pivotal role in various human cognitive functions, including learning, reasoning and communication. There is very little work on endowing machines with the ability to form and reason with concepts. In this work, we analyze how well contemporary large language models (LLMs) capture human concepts and their structure.
  arXiv  Detail & Related papers  (2023-11-03T12:19:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.