Exploring Response Uncertainty in MLLMs: An Empirical Evaluation under Misleading Scenarios

ACL ARR 2025 February Submission1445 Authors

Abstract:

Ensuring that Multimodal Large Language Models (MLLMs) maintain consistency in their responses is essential for developing trustworthy multimodal intelligence. However, existing benchmarks include many samples where all MLLMs \textit{exhibit high response uncertainty when encountering misleading information}, requiring even 5-15 response attempts per sample to effectively assess uncertainty.
Therefore, we propose a two-stage pipeline: first, we collect MLLMs' responses without misleading information, and then gather misleading ones via specific misleading instructions. By calculating the misleading rate, and capturing both correct-to-incorrect and incorrect-to-correct shifts between the two sets of responses, we can effectively metric the model's response uncertainty.
Eventually, we establish a **Multimodal Uncertainty Benchmark (MUB) that employs both explicit and implicit misleading instructions to comprehensively assess the vulnerability of MLLMs across diverse domains**. Our experiments reveal that all open-source and close-source MLLMs are highly susceptible to misleading instructions, with an average misleading rate exceeding 86\%. To enhance the robustness of MLLMs, we further fine-tune all open-source MLLMs by incorporating explicit and implicit misleading data, which demonstrates a significant reduction in misleading rates.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: Response Uncertainty, MLLMs

Contribution Types: Model analysis & interpretability, Data resources

Languages Studied: English

Submission Number: 1445