4 citations · 7 across the 6 of their papers we have counts for
6 papers
Memorization Sinks: Isolating Memorization during LLM Training
Gaurav R. Ghosal, Pratyush Maini, Aditi Raghunathan
Large language models are susceptible to memorizing repeated sequences, posing privacy and copyright concerns. A popular mitigation strategy is to remove memorized information from…
Understanding Finetuning for Factual Knowledge Extraction
Gaurav Ghosal, Tatsunori Hashimoto, Aditi Raghunathan
In this work, we study the impact of QA fine-tuning data on downstream factuality. We show that fine-tuning on lesser-known facts that are poorly stored during pretraining yields s…
A Generalized Acquisition Function for Preference-based Reward Learning
Evan Ellis, Gaurav R. Ghosal, Stuart J. Russell +2
Preference-based reward learning is a popular technique for teaching robots and autonomous systems how a human user wants them to perform a task. Previous works have shown that act…
Contextual Reliability: When Different Features Matter in Different Contexts
Gaurav Ghosal, Amrith Setlur, Daniel S. Brown +2
Deep neural networks often fail catastrophically by relying on spurious correlations. Most prior work assumes a clear dichotomy into spurious and reliable features; however, this i…
The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types
Gaurav R. Ghosal, Matthew Zurek, Daniel S. Brown +1
When inferring reward functions from human behavior (be it demonstrations, comparisons, physical corrections, or e-stops), it has proven useful to model the human as making noisy-r…
Multi-Stage Prototype Learning for Interpretable Time Series Classification
Bhavesh Kalisetti, Vincent Wang, Gaurav R. Ghosal +2
Deep learning methods are powerful tools in classifying multivariate time series data. Despite their high performance, these methods are hard to interpret, which diminishes their a…