5 citations · 7 across the 7 of their papers we have counts for
8 papers
An Efficient and Modular Framework for Targeted Harm Mitigation in LLMS
Roberto Campbell, Momin Abbas, Momin Abbass +6
Large Language Models (LLMs) are powerful zero-shot learners but remain prone to misalignment with human preferences, often producing biased, toxic, or otherwise harmful outputs. E…
Exploring Human Perceptions of AI Responses: Insights from a Mixed-Methods Study on Risk Mitigation in Generative Models
Heloisa Candello, Muneeza Azmat, Uma Sushmitha Gunturi +7
With the rapid uptake of generative AI, investigating human perceptions of generated responses has become crucial. A major challenge is their `aptitude' for hallucinating and gener…
A Comprehensive Evaluation framework of Alignment Techniques for LLMs
Muneeza Azmat, Momin Abbas, Maysa Malfiza Garcia de Macedo +9
As Large Language Models (LLMs) become increasingly integrated into real-world applications, ensuring their outputs align with human values and safety standards has become critical…
Speculate, then Collaborate: Fusing Knowledge of Language Models during Decoding
Ziyao Wang, Muneeza Azmat, Ang Li +2
Large Language Models (LLMs) often excel in specific domains but fall short in others due to the limitations of their training. Thus, enabling LLMs to solve problems collaborativel…
SPRI: Aligning Large Language Models with Context-Situated Principles
Hongli Zhan, Muneeza Azmat, Raya Horesh +2
Aligning Large Language Models to integrate and reflect human values, especially for tasks that demand intricate human oversight, is arduous since it is resource-intensive and time…
Out-of-Distribution Detection using Synthetic Data Generation
Momin Abbas, Muneeza Azmat, Raya Horesh +1
Distinguishing in- and out-of-distribution (OOD) inputs is crucial for reliable deployment of classification systems. However, OOD data is typically unavailable or difficult to col…
A SWAT-based Reinforcement Learning Framework for Crop Management
Malvern Madondo, Muneeza Azmat, Kelsey Dipietro +5
Crop management involves a series of critical, interdependent decisions or actions in a complex and highly uncertain environment, which exhibit distinct spatial and temporal variat…
Forecasting Soil Moisture Using Domain Inspired Temporal Graph Convolution Neural Networks To Guide Sustainable Crop Management
Muneeza Azmat, Malvern Madondo, Kelsey Dipietro +5
Climate change, population growth, and water scarcity present unprecedented challenges for agriculture. This project aims to forecast soil moisture using domain knowledge and machi…