6 citations · 10 across the 5 of their papers we have counts for
5 papers
Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions
Seyedali Mohammadi, Bhaskara Hanuma Vedula, Hemank Lamba +4
Do LLMs genuinely incorporate external definitions, or do they primarily rely on their parametric knowledge? To address these questions, we conduct controlled experiments across mu…
Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection
Siddhant Gupta, Fred Lu, Andrew Barlow +5
A strategy used by malicious actors is to "live off the land," where benign systems and tools already available on a victim's systems are used and repurposed for the malicious acto…
High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates
Fred Lu, Ryan R. Curtin, Edward Raff +2
As the size of datasets used in statistical learning continues to grow, distributed training of models has attracted increasing attention. These methods partition the data and expl…
Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification
Fred Lu, Ryan R. Curtin, Edward Raff +2
While distributed training is often viewed as a solution to optimizing linear models on increasingly large datasets, inter-machine communication costs of popular distributed approa…
WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions
Seyedali Mohammadi, Edward Raff, Jinendra Malekar +3
Language Models (LMs) are being proposed for mental health applications where the heightened risk of adverse outcomes means predictive performance may not be a sufficient litmus te…