Understanding AI Evaluation Patterns: How Different GPT Models Assess Vision-Language Descriptions

• URL: http://arxiv.org/abs/2509.10707v2
• Date: Fri, 19 Sep 2025 14:57:35 GMT
• Title: Understanding AI Evaluation Patterns: How Different GPT Models Assess Vision-Language Descriptions
• Authors: Sajjad Abdoli, Rudi Cilibrasi, Rima Al-Shikh,
• Abstract summary: This study analyzes vision-language descriptions generated by NVIDIA's Describe Anything Model.<n>Three GPT variants (GPT-4o, GPT-4o-mini, GPT-5) are evaluated to uncover distinct "evaluation personalities"<n>GPT-4o-mini exhibits systematic consistency with minimal variance, GPT-4o excels at error detection, while GPT-5 shows extreme conservatism with high variability.
• Score: 0.4078247440919473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As AI systems increasingly evaluate other AI outputs, understanding their assessment behavior becomes crucial for preventing cascading biases. This study analyzes vision-language descriptions generated by NVIDIA's Describe Anything Model and evaluated by three GPT variants (GPT-4o, GPT-4o-mini, GPT-5) to uncover distinct "evaluation personalities" the underlying assessment strategies and biases each model demonstrates. GPT-4o-mini exhibits systematic consistency with minimal variance, GPT-4o excels at error detection, while GPT-5 shows extreme conservatism with high variability. Controlled experiments using Gemini 2.5 Pro as an independent question generator validate that these personalities are inherent model properties rather than artifacts. Cross-family analysis through semantic similarity of generated questions reveals significant divergence: GPT models cluster together with high similarity while Gemini exhibits markedly different evaluation strategies. All GPT models demonstrate a consistent 2:1 bias favoring negative assessment over positive confirmation, though this pattern appears family-specific rather than universal across AI architectures. These findings suggest that evaluation competence does not scale with general capability and that robust AI assessment requires diverse architectural perspectives.

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