Unlocking Knowledge in Transformer Models
Explore how we reverse-engineer factual knowledge in transformer architectures through innovative interpretability methods.
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Innovative Research in Mechanistic Interpretability
We explore how knowledge is stored in transformers through systematic analysis and precision experiments, focusing on attention patterns and activation contributions during factual question-answering tasks.
Transformative insights into AI understanding.
Kim Dorsey
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Mechanistic Interpretability Services
Explore how knowledge is stored and retrieved in transformer architectures through our experimental methods.
Precision Experiments
We conduct controlled experiments to analyze factual datasets and isolate key variables affecting performance.
Attention Analysis
Our systematic analysis of attention head patterns enhances understanding of model behavior during question-answering tasks.
Get In Touch
Contact us to discuss our mechanistic interpretability methods and research on transformer architectures.