2 citations · 2 across the 2 of their papers we have counts for
5 papers
Beyond Static Evaluation: Rethinking the Assessment of Personalized Agent Adaptability in Information Retrieval
Kirandeep Kaur, Preetam Prabhu Srikar Dammu, Hideo Joho +1
Personalized AI agents are becoming central to modern information retrieval, yet most evaluation methodologies remain static, relying on fixed benchmarks and one-off metrics that f…
QLENS: Towards A Quantum Perspective of Language Transformers
Aditya Gupta, Kirandeep Kaur, Vinayak Gupta +1
In natural language processing, current methods for understanding Transformers are successful at identifying intermediate predictions during a model's inference. However, these app…
Dynamic Evaluation Framework for Personalized and Trustworthy Agents: A Multi-Session Approach to Preference Adaptability
Chirag Shah, Hideo Joho, Kirandeep Kaur +1
Recent advancements in generative AI have significantly increased interest in personalized agents. With increased personalization, there is also a greater need for being able to tr…
Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendations
Kirandeep Kaur, Vinayak Gupta, Manya Chadha +1
Conventional recommendation systems (RSs) are typically optimized to enhance performance metrics uniformly across all training samples, inadvertently overlooking the needs of diver…
Efficient and Responsible Adaptation of Large Language Models for Robust Top-k Recommendations
Kirandeep Kaur, Chirag Shah
Conventional recommendation systems (RSs) are typically optimized to enhance performance metrics uniformly across all training samples. This makes it hard for data-driven RSs to ca…