4 papers · 1 filter
MPSelectTune: Prompt-type Selection for Fine-tuning improves Concept Unlearning in LLMs
Shubhadip Nag, Srinjoy Das, Agniva Saha +5
LLMs can be conveniently adapted to a diverse set of tasks, e.g, prediction, question-answering tasks, etc, using appropriate prompts with few-shot examples. Biased or harmful conc…
Forecast2Anomaly (F2A): Adapting Multivariate Time Series Foundation Models for Anomaly Prediction
Atif Hassan, Tarun Kumar, Ashish Mishra +7
Forecasting anomalies (anomaly prediction) in multivariate time series from different real-world, dynamic, and complex systems is vital for preempting critical failures, leading to…
Data Efficient Evaluation of Large Language Models and Text-to-Image Models via Adaptive Sampling
Cong Xu, Gayathri Saranathan, Mahammad Parwez Alam +5
Evaluating LLMs and text-to-image models is a computationally intensive task often overlooked. Efficient evaluation is crucial for understanding the diverse capabilities of these m…
VTruST: Controllable value function based subset selection for Data-Centric Trustworthy AI
Soumi Das, Shubhadip Nag, Shreyyash Sharma +2
Trustworthy AI is crucial to the widespread adoption of AI in high-stakes applications with fairness, robustness, and accuracy being some of the key trustworthiness metrics. In thi…