5 citations · 10 across the 12 of their papers we have counts for
13 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…
DECASTE: Unveiling Caste Stereotypes in Large Language Models through Multi-Dimensional Bias Analysis
Prashanth Vijayaraghavan, Soroush Vosoughi, Lamogha Chiazor +4
Recent advancements in large language models (LLMs) have revolutionized natural language processing (NLP) and expanded their applications across diverse domains. However, despite t…
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…
CharED: Character-wise Ensemble Decoding for Large Language Models
Kevin Gu, Eva Tuecke, Dmitriy Katz +3
Large language models (LLMs) have shown remarkable potential for problem solving, with open source models achieving increasingly impressive performance on benchmarks measuring area…
CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologies
Weiyan Shi, Ryan Li, Yutong Zhang +5
To enhance language models' cultural awareness, we design a generalizable pipeline to construct cultural knowledge bases from different online communities on a massive scale. With…
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor +35
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations. Due to several limiting factors surrounding LLMs (trai…
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…
Financial Forecasting and Analysis for Low-Wage Workers
Wenyu Zhang, Raya Horesh, Karthikeyan N. Ramamurthy +4
Despite the plethora of financial services and products on the market nowadays, there is a lack of such services and products designed especially for the low-wage population. Appro…