So, what is #MechanisticInterpretability 🤔
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So, what is #MechanisticInterpretability 🤔
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Mechanistic Interpretability (MI) is the discipline of opening the black box of large language models (and other neural networks) to understand the underlying circuits, features and/or mechanisms that give rise to specific behaviours
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Mechanistic Interpretability (MI) is the discipline of opening the black box of large language models (and other neural networks) to understand the underlying circuits, features and/or mechanisms that give rise to specific behaviours
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Instead of treating a model as a monolithic function, we can:
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Instead of treating a model as a monolithic function, we can:
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1. Trace how input tokens propagate through attention heads & MLP layers
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1. Trace how input tokens propagate through attention heads & MLP layers
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2. Identify localized “circuit motifs” 
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2. Identify localized “circuit motifs” 
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3. Develop methods to systematically break down or “edit” these circuits to confirm we understand the causal structure.
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3. Develop methods to systematically break down or “edit” these circuits to confirm we understand the causal structure.
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Mechanistic Interpretability aims to yield human-understandable explanations of how advanced models represent and manipulate concepts which hopefully leads to
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Mechanistic Interpretability aims to yield human-understandable explanations of how advanced models represent and manipulate concepts which hopefully leads to
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1. Trust & Reliability
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1. Trust & Reliability
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2. Safety & Alignment
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2. Safety & Alignment
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3. Better Debugging / Development Insights
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3. Better Debugging / Development Insights
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https://bsky.app/profile/mechanistics.bsky.social/post/3lgvvv72uls2x
https://bsky.app/profile/mechanistics.bsky.social/post/3lgvvv72uls2x
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So, what is #MechanisticInterpretability 🤔

Mechanistic Interpretability (MI) is the discipline of opening the black box of large language models (and other neural networks) to understand the underlying circuits, features and/or mechanisms that give rise to specific behaviours

Instead of treating a model as a monolithic function, we can:

1. Trace how input tokens propagate through attention heads & MLP layers
2. Identify localized “circuit motifs” 
3. Develop methods to systematically break down or “edit” these circuits to confirm we understand the causal structure.

Mechanistic Interpretability aims to yield human-understandable explanations of how advanced models represent and manipulate concepts which hopefully leads to

1. Trust & Reliability
2. Safety & Alignment
3. Better Debugging / Development Insights

https://bsky.app/profile/mechanistics.bsky.social/post/3lgvvv72uls2x
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