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arXiv Machine Learning By Boyoung Kwon, Jiyoon Seo, Sang Ho Lee

Discovering Structurally Plausible and Interpretable Cognitive Models with Large Language Models

Read the original on arXiv Machine Learning →

The paper introduces DisCo, a framework that uses large language models (LLMs) to automatically generate interpretable symbolic cognitive models. DisCo employs evolutionary search guided by two structural components: a Validator that filters out structurally invalid models and a Cleaner that enhances parameter identifiability by removing redundancies. Experiments on three behavioral tasks show that DisCo produces models with performance comparable to task‑specific baselines, and ablation studies highlight the Validator’s role in maintaining psychologically meaningful structure.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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