2 citations · 5 across the 15 of their papers we have counts for
4 papers · 1 filter
What AstroPT knows about galaxies, and what that can teach us about LLMs
UniverseTBD, :, Kshitij Duraphe +3
Interpretability research increasingly asks when concepts emerge during training and whether linear probes recover real structure, but in language models these claims are hard to v…
The Tell-Tale Trace: Detecting Reasoning Failures in LLMs Using Chain-of-Thought Dynamics
Shashwat Sourav, Aishwarya Balwani
Chain-of-thought (CoT) reasoning improves large language model (LLM) performance while also providing an observable interface to the model's reasoning process. Existing approaches…
f-INE: A Hypothesis Testing Framework for Estimating Influence under Training Randomness
Subhodip Panda, Dhruv Tarsadiya, Shashwat Sourav +2
Influence estimation methods promise to explain and debug machine learning by estimating the impact of individual samples on the final model. Yet, existing methods collapse under t…
Partially Blinded Unlearning: Class Unlearning for Deep Networks a Bayesian Perspective
Subhodip Panda, Shashwat Sourav, Prathosh A. P
In order to adhere to regulatory standards governing individual data privacy and safety, machine learning models must systematically eliminate information derived from specific sub…