8 papers
From Layers to Submodules: Rethinking Granularity in Replacement-Based LLM Compression
Elia Cunegatti, Marcus Vukojevic, Erik Nielsen +1
Post-training compression of Large Language Models (LLMs) removes entire architectural components, either deleting them or replacing them with fitted modules. Existing replacement-…
Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization
Francesco Pio Monaco, Elia Cunegatti, Flavio Vella +1
Post-training model compression is essential for enhancing the portability of Large Language Models (LLMs) while preserving their performance. While several compression approaches…
Hallucination as an Anomaly: Dynamic Intervention via Probabilistic Circuits
Erik Nielsen, Elia Cunegatti, Marcus Vukojevic +1
One of the most critical challenges in Large Language Models is their tendency to hallucinate, i.e., produce factually incorrect responses. Existing approaches show promising resul…
Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-Training
Elia Cunegatti, Leonardo Lucio Custode, Giovanni Iacca
Network pruning focuses on algorithms that aim to reduce a given model's computational cost by removing a subset of its parameters while having minimal impact on performance. Throu…
2SSP: A Two-Stage Framework for Structured Pruning of LLMs
Fabrizio Sandri, Elia Cunegatti, Giovanni Iacca
We propose a novel Two-Stage framework for Structured Pruning (\textsc{2SSP}) for pruning Large Language Models (LLMs), which combines two different strategies of pruning, namely W…
Evolutionary Reinforcement Learning for Interpretable Decision-Making in Supply Chain Management
Stefano Genetti, Alberto Longobardi, Giovanni Iacca
In the context of Industry 4.0, Supply Chain Management (SCM) faces challenges in adopting advanced optimization techniques due to the "black-box" nature of most AI-based solutions…