4 citations · 8 across the 17 of their papers we have counts for
8 papers · 1 filter
Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI
Aladin Djuhera, Fernando Koch, Alecio Binotto
Inference over large-scale foundation models within heterogeneous edge environments necessitates a fundamentally reconfigurable orchestration substrate. Static partitioning of mode…
When Data is the Algorithm: A Systematic Study and Curation of Preference Optimization Datasets
Aladin Djuhera, Farhan Ahmed, Swanand Ravindra Kadhe +3
Aligning large language models (LLMs) is a central objective of post-training, often achieved through reward modeling and reinforcement learning methods. Among these, direct prefer…
Fixing It in Post: A Comparative Study of LLM Post-Training Data Quality and Model Performance
Aladin Djuhera, Swanand Ravindra Kadhe, Syed Zawad +3
Recent work on large language models (LLMs) has increasingly focused on post-training and alignment with datasets curated to enhance instruction following, world knowledge, and spe…
"Don't Do That!": Guiding Embodied Systems through Large Language Model-based Constraint Generation
Amin Seffo, Aladin Djuhera, Masataro Asai +1
Recent advancements in large language models (LLMs) have spurred interest in robotic navigation that incorporates complex spatial, mathematical, and conditional constraints from na…
SafeCOMM: A Study on Safety Degradation in Fine-Tuned Telecom Large Language Models
Aladin Djuhera, Swanand Ravindra Kadhe, Farhan Ahmed +4
Fine-tuning large language models (LLMs) on telecom datasets is a common practice to adapt general-purpose models to the telecom domain. However, little attention has been paid to…
Intelligent Orchestration of Distributed Large Foundation Model Inference at the Edge
Fernando Koch, Aladin Djuhera, Alecio Binotto
Large Foundation Models (LFMs), including multi-modal and generative models, promise to unlock new capabilities for next-generation Edge AI applications. However, performing infere…
SafeMERGE: Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging
Aladin Djuhera, Swanand Ravindra Kadhe, Farhan Ahmed +2
Fine-tuning large language models (LLMs) is a common practice to adapt generalist models to specialized domains. However, recent studies show that fine-tuning can erode safety alig…
A Simultaneous Decoding Approach to Joint State and Message Communications
Xinyang Li, Yiqi Chen, Vlad C. Andrei +3
The capacity-distortion (C-D) trade-offs for joint state and message communications (JSMC) over single- and multi-user channels are investigated, where the transmitters have access…