4 citations · 10 across the 12 of their papers we have counts for
5 papers · 1 filter
Training Neural Networks for Modularity aids Interpretability
Satvik Golechha, Dylan Cope, Nandi Schoots
An approach to improve network interpretability is via clusterability, i.e., splitting a model into disjoint clusters that can be studied independently. We find pretrained models t…
Progress Measures for Grokking on Real-world Tasks
Satvik Golechha
Grokking, a phenomenon where machine learning models generalize long after overfitting, has been primarily observed and studied in algorithmic tasks. This paper explores grokking i…
NICE: To Optimize In-Context Examples or Not?
Pragya Srivastava, Satvik Golechha, Amit Deshpande +1
Recent work shows that in-context learning and optimization of in-context examples (ICE) can significantly improve the accuracy of large language models (LLMs) on a wide range of t…
CataractBot: An LLM-Powered Expert-in-the-Loop Chatbot for Cataract Patients
Pragnya Ramjee, Bhuvan Sachdeva, Satvik Golechha +4
The healthcare landscape is evolving, with patients seeking reliable information about their health conditions and available treatment options. Despite the abundance of information…
Challenges in Mechanistically Interpreting Model Representations
Satvik Golechha, James Dao
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…