Can Humans Dream of Electric Sheep? Human-Written Samples for Fine-Grained Vision-and-Language Hallucination Benchmarking

2026-08-02

AI Summary

This work addresses the limitations of existing hallucination evaluation methods for vision-language models, which predominantly rely on model-generated samples and suffer from poor timeliness and insufficient controllability. To overcome these issues, the authors construct a dataset comprising 1,600 human-written, multilingual hallucination samples, complemented by fine-grained, span-level annotations. Through systematic comparative analysis, they demonstrate for the first time that human-authored samples significantly outperform model-generated ones in terms of distributional similarity, annotation consistency, and content controllability. These advantages enable more stable and generalizable assessment of model hallucination detection capabilities, offering a reliable alternative for benchmarking hallucinations in vision-language systems.

Abstract

In an age of rapid model turnover, how do we make hallucination evaluation more perennial? We explore whether human-written hallucination samples could take the place of model-generated hallucinations, in order to make benchmarking detection independent of particular models. To this end, we construct a dataset of 1,600 human-written samples, spanning four languages (Chinese, English, French, Italian), and 18,400 samples from five vision-and-language models, all annotated for hallucinations using a fine-grained span-level labeling scheme. We find that human-written samples result in higher agreement and allow greater control of dataset contents, while remaining distributionally similar to samples derived from vision-and-language samples and providing a reasonable portrayal of detection capabilities - suggesting that human data is a viable substitute for model-based hallucination benchmarks.

Problem

• hallucination benchmarking
• vision-and-language models
• human-written samples
• model evaluation
• fine-grained annotation

Innovation

• human-written hallucination
• fine-grained benchmarking
• vision-and-language models
• span-level annotation
• model-independent evaluation