SCoOP: Semantic Consistent Opinion Pooling for Uncertainty Quantification in Multiple Vision-Language Model Systems
Summary: arXiv:2603.23853v1 Announce Type: new
Abstract: Combining multiple Vision-Language Models (VLMs) can enhance multimodal reasoning and robustness, but aggregating heterogeneous models’ outputs amplifies uncertainty and increases the risk of hallucinations. We propose SCoOP (Semantic-Consistent Opinion Pooling), a training-free uncertainty quantification (UQ) framework for multi-VLM systems through uncertainty-weighted linear opinion pooling. Unlike prior UQ methods designed for single models, SCoOP explicitly measures collective, system-level uncertainty across multiple VLMs, enabling effective hallucination detection and abstention for highly uncertain samples.
Key Features of SCoOP
- Multi-VLM Aggregation: SCoOP provides a novel approach to aggregate outputs from various Vision-Language Models, enhancing the overall reasoning capabilities of AI systems.
- Uncertainty Measurement: The framework quantifies uncertainty at the system level, allowing for more accurate assessments compared to traditional methods that focus on individual models.
- Efficient Processing: Despite its sophisticated mechanisms, SCoOP introduces only microsecond-level aggregation overhead, making it a practical choice for real-time applications.
Performance Metrics
SCoOP has demonstrated impressive results in various benchmarks, particularly in the ScienceQA dataset. The following metrics highlight its effectiveness:
- Hallucination Detection: Achieved an AUROC score of 0.866, outperforming baseline models that scored between 0.732 and 0.757 by approximately 10-13%.
- Abstention Performance: Attained an AURAC of 0.907, surpassing baseline scores ranging from 0.818 to 0.840 by 7-9%.
Implications for Multimodal AI Systems
The introduction of SCoOP marks a significant advancement in the reliability of multimodal AI systems. By effectively detecting hallucinations and managing uncertainty, this framework enhances the trustworthiness of outputs generated by multiple VLMs. The implications of these advancements are profound, as they pave the way for more robust applications in fields such as healthcare, autonomous driving, and content generation.
Conclusion
In conclusion, SCoOP (Semantic-Consistent Opinion Pooling) offers a promising solution for uncertainty quantification in multi-VLM systems. Its ability to measure collective uncertainty and detect hallucinations positions it as a vital tool for improving the reliability of multimodal AI. As artificial intelligence continues to evolve, frameworks like SCoOP will be essential in ensuring that these systems are not only powerful but also safe and trustworthy.