1 citations · 1 across the 6 of their papers we have counts for
7 papers
Should SLMs be in the Driver's Seat? A Study of Local Models as Interpreters in Cloud-Written Agent Programs
Boris Radovič, Marco Canini, Veljko Pejović
The most accurate agents executing user-provided tasks are driven by large, cloud-hosted models, which solve them by reading the private data of the user. Agents powered by models…
COMED: The Missing Middle Between Routing and Collaboration in Multi-LLM Inference
Norah Alballa, Wenxuan Zhang, Salma Kharrat +4
No single Large Language Model (LLM) is uniformly reliable across queries, motivating multi-model inference systems that either route among models or combine their outputs. However…
\k{appa}-LoRA: Condition Numbers Reveal Which LoRA Matrices Worth Updating
Jianghui Wang, Silong Yong, Francesco Orabona +3
Low-Rank Adaptation (LoRA) has become a widely adopted technique for efficient neural network fine-tuning, decomposing model updates into low-rank matrices. However, LoRA remains c…
Why Smaller Is Slower? Dimensional Misalignment in Compressed LLMs
Jihao Xin, Tian Lyu, Qilong Pan +2
Post-training compression reduces LLM parameter counts but often produces irregular tensor dimensions that degrade GPU performance -- a phenomenon we call \emph{dimensional misalig…
ACING: Actor-Critic for Instruction Learning in Black-Box LLMs
Salma Kharrat, Fares Fourati, Marco Canini
The effectiveness of Large Language Models (LLMs) in solving tasks depends significantly on the quality of their instructions, which often require substantial human effort to craft…
Where is the Testbed for my Federated Learning Research?
Janez Božič, Amândio R. Faustino, Boris Radovič +2
Progressing beyond centralized AI is of paramount importance, yet, distributed AI solutions, in particular various federated learning (FL) algorithms, are often not comprehensively…
Decentralized Personalized Federated Learning
Salma Kharrat, Marco Canini, Samuel Horvath
This work tackles the challenges of data heterogeneity and communication limitations in decentralized federated learning. We focus on creating a collaboration graph that guides eac…