Human-Inspired Learning for Large Language Models via Obvious Record and Maximum-Entropy Method Discovery

TL;DR

This paper introduces a human-inspired learning framework for LLMs that explicitly stores cause-effect relationships and discovers diverse methods, improving generalization to rare and unseen scenarios.

Contribution

It proposes Obvious Record and Maximum-Entropy Method Discovery mechanisms, enabling explicit memory and diverse method discovery in large language models.

Findings

Achieves better coverage of unseen questions

Increases internal diversity of learned methods

Demonstrates effectiveness on diverse question-answer pairs

Abstract

Large Language Models (LLMs) excel at extracting common patterns from large-scale corpora, yet they struggle with rare, low-resource, or previously unseen scenarios-such as niche hardware deployment issues or irregular IoT device behaviors-because such cases are sparsely represented in training data. Moreover, LLMs rely primarily on implicit parametric memory, which limits their ability to explicitly acquire, recall, and refine methods, causing them to behave predominantly as intuition-driven predictors rather than deliberate, method-oriented learners. Inspired by how humans learn from rare experiences, this paper proposes a human-inspired learning framework that integrates two complementary mechanisms. The first, Obvious Record, explicitly stores cause--result (or question--solution) relationships as symbolic memory, enabling persistent learning even from single or infrequent encounters. The second, Maximum-Entropy Method Discovery, prioritizes and preserves methods with high semantic dissimilarity, allowing the system to capture diverse and underrepresented strategies that are typically overlooked by next-token prediction. Verification on a benchmark of 60 semantically diverse question--solution pairs demonstrates that the proposed entropy-guided approach achieves stronger coverage of unseen questions and significantly greater internal diversity than a random baseline, confirming its effectiveness in discovering more generalizable and human-inspired methods.