4 citations · 5 across the 5 of their papers we have counts for
6 papers
OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics
Vineeth Dorna, Anmol Mekala, Wenlong Zhao +4
Robust unlearning is crucial for safely deploying large language models (LLMs) in environments where data privacy, model safety, and regulatory compliance must be ensured. Yet the…
Does quantization affect models' performance on long-context tasks?
Anmol Mekala, Anirudh Atmakuru, Yixiao Song +2
Large language models (LLMs) now support context windows exceeding 128K tokens, but this comes with significant memory requirements and high inference latency. Quantization can mit…
Alternate Preference Optimization for Unlearning Factual Knowledge in Large Language Models
Anmol Mekala, Vineeth Dorna, Shreya Dubey +5
Machine unlearning aims to efficiently eliminate the influence of specific training data, known as the forget set, from the model. However, existing unlearning methods for Large La…
Automated Model Selection for Tabular Data
Avinash Amballa, Gayathri Akkinapalli, Manas Madine +2
Structured data in the form of tabular datasets contain features that are distinct and discrete, with varying individual and relative importances to the target. Combinations of one…
Using Early Readouts to Mediate Featural Bias in Distillation
Rishabh Tiwari, Durga Sivasubramanian, Anmol Mekala +2
Deep networks tend to learn spurious feature-label correlations in real-world supervised learning tasks. This vulnerability is aggravated in distillation, where a student model may…
DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation
Suraj Kothawade, Anmol Mekala, Chandra Sekhara D +4
State-of-the-art Automatic Speech Recognition (ASR) systems are known to exhibit disparate performance on varying speech accents. To improve performance on a specific target accent…