AMPS: Adaptive Modality Preference Steering via Functional Entropy
arXiv:2602.12533v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) often exhibit significant modality preference, which is a tendency to favor one modality over another. Depending on the input, they may over-rely on linguistic priors relative to visual evidence, or conversely over-attend to visually salient but facts in textual contexts. Prior work has applied a uniform steering intensity to adjust the modality preference of MLLMs. However, strong steering can impair standard inference and increase error rates, whereas weak steering is often ineffective. In addition, because steering sensitivity varies substantially across multimodal instances, a single global strength is difficult to calibrate. To address this limitation with minimal disruption to inference, we introduce an instance-aware diagnostic metric that quantifies each modality's information contribution and reveals sample-specific susceptibility to steering. Building on these insights, we propose a scal
arXiv:2602.12533v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) often exhibit significant modality preference, which is a tendency to favor one modality over another. Depending on the input, they may over-rely on linguistic priors relative to visual evidence, or conversely over-attend to visually salient but facts in textual contexts. Prior work has applied a uniform steering intensity to adjust the modality preference of MLLMs. However, strong steering can impair standard inference and increase error rates, whereas weak steering is often ineffective. In addition, because steering sensitivity varies substantially across multimodal instances, a single global strength is difficult to calibrate. To address this limitation with minimal disruption to inference, we introduce an instance-aware diagnostic metric that quantifies each modality's information contribution and reveals sample-specific susceptibility to steering. Building on these insights, we propose a scaling strategy that reduces steering for sensitive samples and a learnable module that infers scaling patterns, enabling instance-aware control of modality preference. Experimental results show that our instance-aware steering outperforms conventional steering in modulating modality preference, achieving effective adjustment while keeping generation error rates low.
Executive Summary
The article 'AMPS: Adaptive Modality Preference Steering via Functional Entropy' addresses the challenge of modality preference in Multimodal Large Language Models (MLLMs), which tend to favor one modality over another, leading to potential inaccuracies. The authors introduce an instance-aware diagnostic metric to quantify each modality's information contribution and propose a scaling strategy that adjusts steering intensity based on sample sensitivity. This adaptive approach aims to balance modality preference without impairing standard inference or increasing error rates. Experimental results demonstrate the effectiveness of this method in modulating modality preference while maintaining low error rates.
Key Points
- ▸ MLLMs exhibit significant modality preference, favoring one modality over another.
- ▸ Uniform steering intensity can impair inference and increase error rates.
- ▸ The authors propose an instance-aware diagnostic metric to quantify modality information contribution.
- ▸ A scaling strategy and learnable module are introduced to adjust steering intensity based on sample sensitivity.
- ▸ Experimental results show improved modulation of modality preference with low error rates.
Merits
Adaptive Steering
The adaptive steering mechanism allows for fine-tuned control over modality preference, reducing the risk of over-reliance on a single modality.
Instance-Aware Metric
The introduction of an instance-aware diagnostic metric provides a more nuanced understanding of modality contributions, enabling more effective steering.
Experimental Validation
The experimental results validate the effectiveness of the proposed method, demonstrating improved performance in modulating modality preference while maintaining low error rates.
Demerits
Complexity
The proposed method introduces additional complexity to the model, which may require significant computational resources and expertise to implement effectively.
Generalizability
The effectiveness of the method may vary across different types of multimodal data, and further research is needed to ensure its generalizability.
Implementation Challenges
The practical implementation of the learnable module and scaling strategy may pose challenges, particularly in real-world applications where data diversity is high.
Expert Commentary
The article presents a significant advancement in the field of multimodal learning by addressing the critical issue of modality preference in MLLMs. The introduction of an instance-aware diagnostic metric and adaptive steering mechanism represents a sophisticated approach to balancing modality contributions, which is essential for improving model accuracy and reliability. The experimental results provide strong evidence of the method's effectiveness, demonstrating its potential to enhance the performance of MLLMs in various applications. However, the complexity and implementation challenges associated with the proposed method warrant further investigation. Future research should focus on simplifying the implementation process and ensuring the generalizability of the method across diverse multimodal datasets. Additionally, the ethical implications of adaptive steering in multimodal models should be carefully considered, particularly in applications where model transparency and fairness are paramount.
Recommendations
- ✓ Further research should explore the scalability and generalizability of the proposed method across different types of multimodal data and applications.
- ✓ Efforts should be made to simplify the implementation of the learnable module and scaling strategy to facilitate broader adoption in practical settings.