Analyzing Fine-tuning Representation Shift for Multimodal LLMs Steering alignment (2501.03012v1)

Abstract: Multimodal LLMs have reached remarkable levels of proficiency in understanding multimodal inputs, driving extensive research to develop increasingly powerful models. However, much less attention has been paid to understanding and explaining the underlying mechanisms of these models. Most existing explainability research examines these models only in their final states, overlooking the dynamic representational shifts that occur during training. In this work, we systematically analyze the evolution of hidden state representations to reveal how fine-tuning alters the internal structure of a model to specialize in new multimodal tasks. Using a concept-based approach, we map hidden states to interpretable visual and textual concepts, enabling us to trace changes in encoded concepts across modalities as training progresses. We also demonstrate the use of shift vectors to capture these concepts changes. These shift vectors allow us to recover fine-tuned concepts by shifting those in the original model. Finally, we explore the practical impact of our findings on model steering, showing that we can adjust multimodal LLMs behaviors without any training, such as modifying answer types, captions style, or biasing the model toward specific responses. Our work sheds light on how multimodal representations evolve through fine-tuning and offers a new perspective for interpreting model adaptation in multimodal tasks. The code for this project is publicly available at https://github.com/mshukor/xl-vlms.

• The paper demonstrates that fine-tuning reconfigures hidden semantic representations to incorporate task-specific concepts in multimodal LLMs.
• It introduces a novel mapping approach that connects hidden states with interpretable visual and textual features during fine-tuning.
• The study reveals that using concept shift vectors allows steering of model outputs without retraining, reducing computational costs.

Analyzing Fine-tuning Representation Shift for Multimodal LLMs Steering

Overview

The paper entitled "Analyzing Fine-tuning Representation Shift for Multimodal LLMs Steering" presents a methodological exploration into the dynamic changes in representation that occur when multimodal LLMs (MLLMs) are fine-tuned for specific tasks. At its core, this work seeks to elucidate how fine-tuning impacts the semantic understanding of these models and, subsequently, how divergent concepts can guide model steering without further training.

Key Findings

The researchers systematically deconstruct the internal representation shifts that occur during the fine-tuning of MLLMs. Using a novel approach that maps hidden state representations to interpretable visual and textual concepts, they trace the alterations these fine-tuned models undergo. This analysis reveals that:

• Fine-tuning task-specialized models adapt existing concepts and incorporate new ones while discarding others irrelevant to the task.
• The utility of shift vectors emerges as a way to effectively understand and replicate how concepts in fine-tuned models can be translated back to the original model's structure.
• Furthermore, the articulation of these shifts can serve pragmatic purposes, such as steering model behaviors toward desired outputs without the necessity for retraining. This includes modifications in answer types, style biases, or even fine-grained concept-level steering.

Practical and Theoretical Implications

From a practical standpoint, this work highlights an efficient pathway toward model specialization that circumvents the computational costs typically associated with conventional fine-tuning. By introducing concept shift vectors as a steering tool, the authors showcase how foundational models can undergo behavioral adjustments directly through feature editing, thus making the process resource-efficient and flexible.

Theoretically, this research sheds light on the adaptive mechanisms within MLLMs that allow for changes in learned semantic structures. This knowledge bears implications for understanding the internal "thought processes" of these models, offering avenues for future research aimed at enhancing their explainability.

Speculatives on Future Progress

As the domain of AI continues to progress, this research portends possibilities where dynamic model steering based on shift vectors becomes a foundational technique for customizing MLLMs. Further exploration in integrating these methodologies could lead to breakthroughs in crafting models that are not only efficient but also easily adaptable to a myriad of specialized tasks. It also presents the potential for developing more transparent AI systems that can be interpreted and guided in human-understandable ways.

Conclusion

"Analyzing Fine-tuning Representation Shift for Multimodal LLMs Steering" contributes significant insights into the understanding and manipulation of MLLMs. By mapping the evolution of semantic concepts during fine-tuning and enabling model steering without retraining, it opens up new methods for efficient model customization. These advancements not only economize computational resources but also introduce possibilities for more responsible and interpretable AI systems. As this field develops further, we anticipate a future where AI can be dynamically adjusted in real-time, catering to evolving domain-specific requirements with minimal resource expenditure.