Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume
arXiv:2602.24195v1 Announce Type: new Abstract: Despite their capabilities, Multimodal Large Language Models (MLLMs) may produce plausible but erroneous outputs, hindering reliable deployment. Accurate uncertainty metrics could enable escalation of unreliable queries to human experts or larger models for improved performance. However, existing uncertainty metrics have practical constraints, such as being designed only for specific modalities, reliant on external tools, or computationally expensive. We introduce UMPIRE, a training-free uncertainty quantification framework for MLLMs that works efficiently across various input and output modalities without external tools, relying only on the models' own internal modality features. UMPIRE computes the incoherence-adjusted semantic volume of sampled MLLM responses for a given task instance, effectively capturing both the global semantic diversity of samples and the local incoherence of responses based on internal model confidence. We propo
arXiv:2602.24195v1 Announce Type: new Abstract: Despite their capabilities, Multimodal Large Language Models (MLLMs) may produce plausible but erroneous outputs, hindering reliable deployment. Accurate uncertainty metrics could enable escalation of unreliable queries to human experts or larger models for improved performance. However, existing uncertainty metrics have practical constraints, such as being designed only for specific modalities, reliant on external tools, or computationally expensive. We introduce UMPIRE, a training-free uncertainty quantification framework for MLLMs that works efficiently across various input and output modalities without external tools, relying only on the models' own internal modality features. UMPIRE computes the incoherence-adjusted semantic volume of sampled MLLM responses for a given task instance, effectively capturing both the global semantic diversity of samples and the local incoherence of responses based on internal model confidence. We propose uncertainty desiderata for MLLMs and provide theoretical analysis motivating UMPIRE's design. Extensive experiments show that UMPIRE consistently outperforms baseline metrics in error detection and uncertainty calibration across image, audio, and video-text benchmarks, including adversarial and out-of-distribution settings. We also demonstrate UMPIRE's generalization to non-text output tasks, including image and audio generation.
Executive Summary
This article proposes UMPIRE, a training-free uncertainty quantification framework for Multimodal Large Language Models (MLLMs) that efficiently computes the incoherence-adjusted semantic volume of sampled MLLM responses. UMPIRE effectively captures both global semantic diversity and local incoherence of responses based on internal model confidence, outperforming baseline metrics in error detection and uncertainty calibration across various benchmarks, including adversarial and out-of-distribution settings. The proposed framework and extensive experiments demonstrate its potential to provide reliable uncertainty metrics for MLLMs, enabling their reliable deployment and potentially improving model performance through human expert escalation or larger model utilization. The article also discusses uncertainty desiderata for MLLMs and provides theoretical analysis motivating UMPIRE's design, further solidifying its contributions to the field.
Key Points
- ▸ UMPIRE is a training-free uncertainty quantification framework for MLLMs that efficiently computes the incoherence-adjusted semantic volume of sampled MLLM responses.
- ▸ UMPIRE outperforms baseline metrics in error detection and uncertainty calibration across various benchmarks, including adversarial and out-of-distribution settings.
- ▸ The proposed framework and extensive experiments demonstrate its potential to provide reliable uncertainty metrics for MLLMs.
Merits
Innovative Approach
UMPIRE introduces a novel uncertainty quantification framework for MLLMs, addressing the limitations of existing methods and providing a more efficient and effective solution.
Scalability and Generalizability
UMPIRE can be applied to various input and output modalities without external tools, making it a scalable and generalizable solution for MLLMs.
Theoretical Foundations
The article provides theoretical analysis motivating UMPIRE's design, solidifying its contributions to the field and demonstrating a deep understanding of the underlying mechanisms.
Demerits
Limited Evaluation
While the article presents extensive experiments, the evaluation may be limited to specific benchmarks and settings, which could impact the generalizability of the results.
Computational Complexity
UMPIRE's computational complexity is not explicitly discussed, which could be a concern for large-scale deployments or high-performance requirements.
Expert Commentary
The article's contributions to the field of uncertainty quantification in deep learning are significant, and the proposed framework, UMPIRE, demonstrates a novel and effective approach to addressing the challenges of uncertainty quantification in MLLMs. However, the article's limitations, such as the limited evaluation and computational complexity, should be carefully considered in future work. Additionally, the article's findings and implications for policy-making in the field of artificial intelligence are far-reaching and warrant further exploration.
Recommendations
- ✓ Further research is needed to explore the limitations of UMPIRE, particularly in terms of its computational complexity and generalizability across various benchmarks and settings.
- ✓ The article's findings and implications for policy-making in the field of artificial intelligence should be carefully considered and explored in future work.