New metric tracks where multimodal reasoning models go wrong

Over the past decades, computer scientists have introduced increasingly sophisticated machine learning-based models, which can perform remarkably well on various tasks. These include multimodal large language models (MLLMs), systems that can process and generate different types of data, predominantly texts, images and videos.

Some of these models, such as OpenAI’s GPT4 with Vision (GPT-4V), DeepSeek-R1 and Google Gemini, are now widely used by users worldwide to create specific multi-modal content, including images for social media posts or articles, as well as texts tailored for specific uses.

While the reasoning abilities of these models have improved considerably in recent years, allowing them to solve mathematical and reasoning problems, studies showed that they sometimes respond to things that are not grounded in the input data, for instance, by describing details that do not actually exist in an input image.

These hallucinations have been linked to language priors and internal biases that a model may have acquired during training while it was analyzing large text datasets. These biases can override the visual information fed to the model (i.e., input images), causing the model to incorrectly complete the tasks assigned to it.

Researchers at UC Santa Cruz, Stanford University and UC Santa Barbara have recently developed a metric and a diagnostic benchmark that could help to study these hallucinations, specifically focusing on the relationship between the reasoning of MLLMs and their tendency to hallucinate when asked to describe what is portrayed in an input image. These new research tools, presented in a paper on the arXiv preprint server, could contribute to the assessment and advancement of MLLMs.

“Test-time compute has empowered multimodal large language models to generate extended reasoning chains, yielding strong performance on tasks such as multimodal math reasoning,” wrote Chengzhi Liu, Zhongxing Xu and their colleagues in their paper.

“However, this improved reasoning ability often comes with increased hallucination: as generations become longer, models tend to drift away from image-grounded content and rely more heavily on language priors.”

The researchers first assessed the performance of MLLMs on complex reasoning tasks and found that as reasoning chains (i.e., sequences of logical steps required to solve a problem) grew in length, the models’ tendency to hallucinate also increased. They suggested that these hallucinations emerged due to reduced attention to visual stimuli and a greater reliance on language priors.

“Attention analysis shows that longer reasoning chains lead to reduced focus on visual inputs, which contributes to hallucination,” wrote Liu, Xu and their colleagues.

“To systematically study this phenomenon, we introduce RH-AUC, a metric that quantifies how a model’s perception accuracy changes with reasoning length, allowing us to evaluate whether the model preserves visual grounding during reasoning. We also release RH-Bench, a diagnostic benchmark that spans a variety of multimodal tasks, designed to assess the trade-off between reasoning ability and hallucination.”

RH-AUC and RH-Bench, the metrics and benchmarks developed by Liu, Xu and his colleagues, could soon be used by other researchers to evaluate the interplay between the reasoning abilities of specific MLLMs and the risk of hallucinating. Moreover, the observations presented in the team’s paper could guide future efforts aimed at developing models that can reliably tackle complex reasoning tasks without becoming prone to hallucinations.

“Our analysis reveals that larger models typically achieve a better balance between reasoning and perception and that this balance is influenced more by the types and domains of training data than by its overall volume,” wrote Liu, Xu and their colleagues. “These findings underscore the importance of evaluation frameworks that jointly consider both reasoning quality and perceptual fidelity.”

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