2 citations · 2 across the 7 of their papers we have counts for
3 papers · 1 filter
Adaptive Circuit Behavior and Generalization in Mechanistic Interpretability
Jatin Nainani, Sankaran Vaidyanathan, AJ Yeung +2
Mechanistic interpretability aims to understand the inner workings of large neural networks by identifying circuits, or minimal subgraphs within the model that implement algorithms…
CS4: Measuring the Creativity of Large Language Models Automatically by Controlling the Number of Story-Writing Constraints
Anirudh Atmakuru, Jatin Nainani, Rohith Siddhartha Reddy Bheemreddy +4
Evaluating the creativity of large language models (LLMs) in story writing is difficult because LLM-generated stories could seemingly look creative but be very similar to some exis…
Evaluating Brain-Inspired Modular Training in Automated Circuit Discovery for Mechanistic Interpretability
Jatin Nainani
Large Language Models (LLMs) have experienced a rapid rise in AI, changing a wide range of applications with their advanced capabilities. As these models become increasingly integr…