Do Large Language Models Judge Error Severity Like Humans?

TL;DR

This study compares human and LLM assessments of error severity in image descriptions, revealing that most LLMs do not accurately replicate human judgments, with some models showing surprising alignment especially in unimodal settings.

Contribution

It systematically evaluates and compares human and LLM error severity judgments across different error types and modalities, highlighting the strengths and limitations of current models.

Findings

Most LLMs assign low severity to gender errors but high to colour errors, unlike humans.

DeepSeek-V3, a unimodal LLM, aligns closely with human judgments across conditions.

Only Doubao replicates human-like error severity ranking but lacks clear error type distinction.

Abstract

Large Language Models (LLMs) are increasingly used as automated evaluators in natural language generation, yet it remains unclear whether they can accurately replicate human judgments of error severity. In this study, we systematically compare human and LLM assessments of image descriptions containing controlled semantic errors. We extend the experimental framework of van Miltenburg et al. (2020) to both unimodal (text-only) and multimodal (text + image) settings, evaluating four error types: age, gender, clothing type, and clothing colour. Our findings reveal that humans assign varying levels of severity to different error types, with visual context significantly amplifying perceived severity for colour and type errors. Notably, most LLMs assign low scores to gender errors but disproportionately high scores to colour errors, unlike humans, who judge both as highly severe but for different reasons. This suggests that these models may have internalised social norms influencing gender judgments but lack the perceptual grounding to emulate human sensitivity to colour, which is shaped by distinct neural mechanisms. Only one of the evaluated LLMs, Doubao, replicates the human-like ranking of error severity, but it fails to distinguish between error types as clearly as humans. Surprisingly, DeepSeek-V3, a unimodal LLM, achieves the highest alignment with human judgments across both unimodal and multimodal conditions, outperforming even state-of-the-art multimodal models.