4 papers
In-Context Bias Propagation in LLM-Based Tabular Data Generation
Pol G. Recasens, Alberto Gutierrez, Jordi Torres +4
Large Language Models (LLMs) are increasingly used for synthetic tabular data generation through in-context learning (ICL), offering a practical solution for data augmentation in d…
FRIDA: Free-Rider Detection using Privacy Attacks
Pol G. Recasens, Ãdám Horváth, Alberto Gutierrez-Torre +3
Federated learning is increasingly popular as it enables multiple parties with limited datasets and resources to train a machine learning model collaboratively. However, similar to…
Towards Pareto Optimal Throughput in Small Language Model Serving
Pol G. Recasens, Yue Zhu, Chen Wang +5
Large language models (LLMs) have revolutionized the state-of-the-art of many different natural language processing tasks. Although serving LLMs is computationally and memory deman…
Mind the Memory Gap: Unveiling GPU Bottlenecks in Large-Batch LLM Inference
Pol G. Recasens, Ferran Agullo, Yue Zhu +5
Large language models have been widely adopted across different tasks, but their auto-regressive generation nature often leads to inefficient resource utilization during inference.…