Aligning MLLMs with the Latent Structure of Human Cognition via Behavior-Derived Semantic Dimensions
Paper recorded by Signals 4 on 2026-10-30 in cs.CL. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-30 on arXiv · recorded by Signals 4 on 2026-11-02
Category: cs.CL · 自然语言处理 · first seen 2026-11-02
Abstract
Multimodal Large Language Models (MLLMs) have achieved impressive performance across a wide range of reasoning tasks, yet it remains unclear whether their internal representations and decision criteria align with the latent structure of human cognition. In this work, we propose a novel framework to align MLLMs with the multidimensional mental representations underlying human similarity judgements.
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Cite this page: Aligning MLLMs with the Latent Structure of Human Cognition via Behavior-Derived Semantic Dimensions: the #50 most recent of 628 cs.CL papers we have recorded (as of 2026-10-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/aligning-mllms-with-the-latent-structure-of-human-cognition-via-behavior-derived.html
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