Challenges in Mechanistically Interpreting Model Representations (2402.03855v2)

Abstract: Mechanistic interpretability (MI) aims to understand AI models by reverse-engineering the exact algorithms neural networks learn. Most works in MI so far have studied behaviors and capabilities that are trivial and token-aligned. However, most capabilities important for safety and trust are not that trivial, which advocates for the study of hidden representations inside these networks as the unit of analysis. We formalize representations for features and behaviors, highlight their importance and evaluation, and perform an exploratory study of dishonesty representations in `Mistral-7B-Instruct-v0.1'. We justify that studying representations is an important and under-studied field, and highlight several challenges that arise while attempting to do so through currently established methods in MI, showing their insufficiency and advocating work on new frameworks for the same.

• The paper highlights the limitations of current mechanistic interpretability methods and argues for a shift toward population-level representation engineering.
• It details how traditional approaches such as saliency maps and activation patching fall short in deciphering complex neural model dynamics.
• The study proposes new frameworks that integrate linear subspace analysis and cognitive neuroscience perspectives to better explain model behaviors.

Mechanistically Interpreting Model Representations: Challenges and Perspectives

This essay explores the complexities and challenges associated with mechanistically interpreting model representations, based on insights from the paper "Challenges in Mechanistically Interpreting Model Representations" (2402.03855). The paper positions itself within the broader discourse on mechanistic interpretability (MI), highlighting the need for innovative frameworks to paper model representations effectively.

Mechanistic Interpretability and its Context

Mechanistic interpretability (MI) seeks to demystify neural networks by reverse-engineering the algorithms they inherently develop. Traditional interpretability tools, such as saliency maps and activation patching, have yielded insights into relatively simple model functions and capabilities which are predominantly token-aligned. While these tools have advanced our understanding of neural networks, their limitations become evident when applied to the more intricate task of deciphering hidden representations.

Figure 1: Hidden representations inside models have meaningful geometric and semantic interpretations.

Limitations of Current Interpretability Approaches

The paper argues that the prevailing MI methods inadequately address the complexity of model representations. Traditional methods often focus on simplified behaviors which do not fully encapsulate the rich structure of internal model dynamics. This critique is compounded by the fact that many capabilities under investigation could be solved without the complexities of deep learning, questioning the scalability of current MI frameworks to more complex scenarios.

A critical analysis identifies a recurring oversight: an over-reliance on cherry-picked results that may not reflect broader neuronal or data distributions. Furthermore, MI methods struggle with scaling to larger models, again emphasizing the inadequacy of existing frameworks in meeting the demands of complex model interpretability.

Representation Engineering

A thematic pivot from token-level interpretability to representation engineering is recommended, drawing inspiration from cognitive neuroscience approaches. This perspective emphasizes the analysis of model's representational spaces rather than isolated neuronal activities. The framework proposed by \citet{zou2023representation} suggests a shift towards population-level analyses, characterizing representations that underpin behaviors—such as honesty, power-seeking, and harmlessness—in AI models. While innovative, these approaches raise ongoing questions about their ability to definitively explain model functionalities.

Evaluating Representations and Behaviors

The exploratory studies within the paper focus on linear subspaces associated with specific behaviors, like "honesty," using methods like principal component analysis of activations. Such directions aim to map complex model behaviors across different layers, searching for emergent linear patterns that explain model outputs.

Figure 2: Cosine similarities of dishonesty directions for each layer.

Evaluating representations involves dimensions such as data splitting and the direct contribution of these representations to model outputs. The paper provides evidence that linear directions—which can align consistently across model layers—might steer model behavior significantly when manipulated.

Activation Patching and Representation Challenges

Activation patching experiments uncover the dense involvement of model components in adopting directional manifestations, aligning with observed behaviors. Importantly, the experiments indicate that representations do not simply boost "dishonest" logits but instead require sustained injection to maintain behavior throughout token generation.

Figure 3: Activation Patching for attention heads.

Mechanistic exploration, such as direct logit attribution, emphasizes how model representations translate into behavior, though many questions remain unresolved. Patching methods face challenges, notably in isolating component responsibilities due to the high dimensionality of the model's functions and potential polysemic interpretations.

Unanswered Questions and Future Directions

The paper concludes by acknowledging the gaps in current capabilities to mechanistically interpret representations authentically. Future research should focus on establishing comprehensive frameworks that integrate the complex and often non-linear nature of model representations.

Figure 4: Contribution of block components to dishonesty direction for different layers.

Ultimately, understanding why models form specific internal representations remains elusive, necessitating new methodologies to capture emergent properties dynamically throughout model training and deployment.

Conclusion

This paper underscores the necessity for a paradigm shift in mechanistic interpretability research to address the intricate layers of model representations. By advocating for enhanced frameworks and methodologies, the research invites the AI community to refocus efforts on an integrative approach that elucidates the complexities of neural networks beyond surface-level interpretation.