RelP: Faithful and Efficient Circuit Discovery in Language Models via Relevance Patching
PositiveArtificial Intelligence
A new method called Relevance Patching (RelP) has been introduced to improve the efficiency of circuit discovery in language models. This approach addresses the limitations of traditional activation patching, which is often too slow for large-scale applications, and the noise issues associated with attribution patching. By providing a more reliable and faster alternative, RelP could significantly enhance our understanding of how language models operate, making it easier for researchers to interpret and refine these complex systems.
— Curated by the World Pulse Now AI Editorial System


