Mechanistic? (2410.09087v1)

Abstract: The rise of the term "mechanistic interpretability" has accompanied increasing interest in understanding neural models -- particularly LLMs. However, this jargon has also led to a fair amount of confusion. So, what does it mean to be "mechanistic"? We describe four uses of the term in interpretability research. The most narrow technical definition requires a claim of causality, while a broader technical definition allows for any exploration of a model's internals. However, the term also has a narrow cultural definition describing a cultural movement. To understand this semantic drift, we present a history of the NLP interpretability community and the formation of the separate, parallel "mechanistic" interpretability community. Finally, we discuss the broad cultural definition -- encompassing the entire field of interpretability -- and why the traditional NLP interpretability community has come to embrace it. We argue that the polysemy of "mechanistic" is the product of a critical divide within the interpretability community.

• The paper’s main contribution is classifying four distinct interpretations of mechanistic interpretability into narrow and broad technical and cultural categories.
• It traces the evolution of interpretability from AI safety origins to modern NLP, emphasizing shifts in methodology and community identity.
• The paper highlights underlying tensions and advocates for a unified framework to bridge epistemological divides in neural model interpretability.

Mechanistic Interpretability: Definitions and Community Dynamics

The paper "Mechanistic?" by Naomi Saphra and Sarah Wiegreffe provides a comprehensive analysis of the term "mechanistic interpretability" and its implications within the field of neural model interpretability, particularly concerning LLMs. The authors delve into the various interpretations of "mechanistic" and explore the historical and cultural contexts that have led to its current usage.

Definitions and Interpretations

The term "mechanistic interpretability" has multiple definitions within the interpretability research community. The authors classify these into four categories:

1. Narrow Technical Definition: Focuses on understanding neural networks through their causal mechanisms.
2. Broad Technical Definition: Encompasses any research examining the internals of a model, such as activations or weights.
3. Narrow Cultural Definition: Pertains to research emerging specifically from the mechanistic interpretability (MI) community.
4. Broad Cultural Definition: Encompasses the entire field of AI interpretability, particularly concerning LLMs.

The narrow technical definition, grounded in causality, aims to distill explanations through causal models. However, this definition contrasts with broader interpretations that allow flexibility in methodological approaches, highlighting the semantic drift influenced by cultural dynamics.

Historical Context and Community Formation

Mechanistic interpretability emerged distinctly relative to earlier NLP interpretability efforts. Initially, NLP interpretability focused on recurrent models, using methods like representational similarity, attention analysis, and probing. These methods often intersect with modern MI techniques, which emphasize understanding model components like neurons and network circuits.

The history of the MI community is marked by its origins in the AI safety and alignment domains, which later transitioned into NLP without extensive engagement with established NLP interpretability researchers. This shift brought about a new cultural identity, focused on understanding LMs and driven by concerns around AI safety.

Community Dynamics and Tensions

The integration of the MI community into NLP interpretability has not been without challenges. Cultural and epistemological differences have led to disputes over research novelty and recognition of prior work. The MI community's methods, sometimes perceived as rediscovering existing knowledge, have sparked debates regarding the efficacy and true novelty of the approaches.

Despite these tensions, the MI community has contributed significantly to the field's growth and dynamism. The increased attention and resources have broadened the appeal and engagement in interpretability research.

Implications and Future Directions

The polysemy of "mechanistic" highlights the underlying cultural divide within the interpretability community. This division, if not addressed, could hinder the potential for collaborative progress. However, the paper suggests avenues for bridging the gap, encouraging greater integration and exchange between different research paradigms.

Looking forward, unifying the standard definitions and frameworks could enhance cohesion and scientific advancement. The community stands to benefit from a more harmonious discourse that leverages the strengths of both traditional NLP interpretability and the energetic contributions of the MI community.

Conclusion

The paper "Mechanistic?" underscores the complexity of defining and operationalizing mechanistic interpretability. By unpacking the term's various interpretations and contextualizing them within the broader interpretability discourse, the authors call for a more inclusive and unified research community. As AI models continue to evolve, fostering an integrated approach to interpretability will be essential to unlocking deeper insights into neural networks.