Large Multi-modal Models Can Interpret Features in Large Multi-modal Models (2411.14982v1)

Abstract: Recent advances in Large Multimodal Models (LMMs) lead to significant breakthroughs in both academia and industry. One question that arises is how we, as humans, can understand their internal neural representations. This paper takes an initial step towards addressing this question by presenting a versatile framework to identify and interpret the semantics within LMMs. Specifically, 1) we first apply a Sparse Autoencoder(SAE) to disentangle the representations into human understandable features. 2) We then present an automatic interpretation framework to interpreted the open-semantic features learned in SAE by the LMMs themselves. We employ this framework to analyze the LLaVA-NeXT-8B model using the LLaVA-OV-72B model, demonstrating that these features can effectively steer the model's behavior. Our results contribute to a deeper understanding of why LMMs excel in specific tasks, including EQ tests, and illuminate the nature of their mistakes along with potential strategies for their rectification. These findings offer new insights into the internal mechanisms of LMMs and suggest parallels with the cognitive processes of the human brain.

• The paper demonstrates that large multimodal model features can be interpreted and steered using a two-step process involving Sparse Autoencoders and an auto-explanation pipeline.
• It reports strong numerical results, including high IoU scores and consistency with both human judgments and GPT-generated explanations.
• The framework offers practical mechanisms for controlling model behavior, enhancing reliability, and mitigating biases in AI applications.

Insights into the Interpretation and Steering of Large Multimodal Models

The paper "Large Multi-modal Models Can Interpret Features in Large Multi-modal Models" presents an innovative framework aimed at demystifying the internal neural representations of Large Multimodal Models (LMMs). This research addresses the opacity of these models by proposing a method for interpreting and influencing their complex neural features. The significance of this work lies not only in providing insights into the functionality and behavior of LMMs but also in offering mechanisms to harness these insights for practical interventions and theoretical advancements.

The paper introduces a two-step process for understanding LMM features using Sparse Autoencoders (SAEs). Initially, an SAE is employed to disentangle model representations into comprehensible features. Subsequently, an auto-explanation pipeline is proposed, leveraging the interpretive capacity of larger LMMs to elucidate the semantic dimensions of these features. The research focuses on the LLaVA-NeXT-8B model, using LLaVA-OV-72B for analysis, demonstrating that these features can be effectively manipulated to alter the model's behavior.

A key highlight of the paper is the ability to steer the model by clamping specific features to high values, thereby influencing the model's responses. This capability is particularly relevant in scenarios requiring adjustment to incorrect behaviors or the enhancement of desired outputs. Such interventions offer promising directions for applications where model reliability and predictability are critical, such as in mitigating hallucinations or biases in model outputs.

The methodological rigor of the paper is evident in the comprehensive approach to feature interpretation and attribution. The research distinguishes itself by addressing challenges specific to LMMs, such as the polysemantic nature of their neural representations and their vast conceptual scope. The authors' development of a zero-shot pipeline for detecting and interpreting open-semantic features significantly minimizes the manual effort traditionally required in such analyses, thus broadening the potential for scalable applications.

In the evaluation of this interpretive framework, the authors report strong numerical results, particularly in terms of Intersection over Union (IoU) scores for various visual concepts. This quantitative analysis reinforces the framework's efficacy in aligning model features with human-understandable concepts. Moreover, the consistency scores with human judgment and GPT-generated explanations further validate the interpretability and coherence of the framework's outputs.

The implications of this research extend into both practical and theoretical domains. Practically, by enabling fine-grained control over model behavior, this framework offers potential for applications in AI safety, user interaction design, and adaptive learning systems. Theoretically, the work prompts further exploration into parallels between LMM features and cognitive processes in the human brain, suggesting avenues for interdisciplinary research that bridges artificial and natural intelligence.

The paper also opens pathways for future developments in AI interpretability. The framework's adaptability to larger or different types of multimodal models, potential enhancements in feature extraction techniques, and integration with even more advanced LLMs are areas ripe for exploration. Additionally, addressing the limitations related to computational resources and dataset diversity could further refine the accuracy and applicability of the interpretive pipeline.

In summary, this research presents a structured, methodological contribution to understanding and controlling Large Multimodal Models. Its blend of theoretical innovation and practical applicability marks a step forward in the field of AI interpretability, with promising implications for future research and AI system design.