4 papers
Action-On-Item Preference Flow: A Shared Event Schema for Predictive and Generative Personalization
Parthiv Chatterjee, Kashish Kanjaria, Vashisth Purani +2
A user's movie, news, and dialogue histories differ in their native actions and outputs, yet each interaction supplies evidence that can update user memory. We study whether these…
Not All Is Lost: Repairing Lossy User Preference States of Personalization Encoders
Parthiv Chatterjee, Dhiraj Golhar, Ummesalma Diwan +3
Personalization encoders compress evolving interaction histories into preference states used to rank items or condition text generation. A task head operating only on this state ca…
Diversity Augmentation of Dynamic User Preference Data for Boosting Personalized Text Summarizers
Parthiv Chatterjee, Shivam Sonawane, Amey Hengle +3
Document summarization enables efficient extraction of user-relevant content but is inherently shaped by individual subjectivity, making it challenging to identify subjective salie…
Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning
Prateek Chanda, Saral Sureka, Parth Pratim Chatterjee +3
Supervised fine-tuning performance for large language models depends strongly on how training budget is distributed across a heterogeneous set of tasks. In practice, mixtures are o…