Paper page - Perception or Prejudice: Can MLLMs Go Beyond First Impressions of Personality?

Source: https://huggingface.co/papers/2605.22109 Authors:

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Abstract

Researchers introduce a new task and dataset for evaluating personality reasoning in multimodal language models, revealing significant gaps between accurate predictions and grounded reasoning processes.

Multimodal Large Language Models(MLLMs) are increasingly deployed in human-facing roles where personality perception is critical, yet existing benchmarks evaluate this capability solely on numericalBig Five score prediction, leaving open whether models truly perceive personality through behavioral understanding or merely prejudge through superficial pattern matching. We address this gap with three contributions. (i) A new task: we formalizeGrounded Personality Reasoning(GPR), which requires MLLMs to anchor each Big Five rating in observable evidence through achain of rating,reasoning,and grounding. (ii) A new dataset: we release MM-OCEAN (1,104 videos, 5,320 MCQs), produced by amulti-agent pipelinewith human verification, with timestampedbehavioral observations,evidence-grounded trait analyses, and seven categories ofcue-grounding MCQs. (iii) Benchmark and analysis: we design athree-tier evaluation(rating,reasoning, grounding) plus four sample-level failure-mode metrics:Prejudice Rate(PR),Confabulation Rate(CR),Integration-failure Rate(IR), andHolistic-grounding Rate(HR), and benchmark 27 MLLMs (13 closed, 14 open). The analysis uncovers a striking Prejudice Gap: across the field, 51% of correct ratings are not grounded in retrieved cues, and theHolistic-Grounding Ratespans only 0-33.5%. These findings expose a disconnect between getting the right score andreasoningfor the right reason, charting a roadmap for grounded social cognition in MLLMs.

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