13 citations · 14 across the 3 of their papers we have counts for
4 papers
SAFE: A Sparse Autoencoder-Based Framework for Robust Query Enrichment and Hallucination Mitigation in LLMs
Samir Abdaljalil, Filippo Pallucchini, Andrea Seveso +3
Despite the state-of-the-art performance of Large Language Models (LLMs), these models often suffer from hallucinations, which can undermine their performance in critical applicati…
Designing Role Vectors to Improve LLM Inference Behaviour
Daniele Potertì, Andrea Seveso, Fabio Mercorio
The influence of personas on Large Language Models (LLMs) has been widely studied, yet their direct impact on performance remains uncertain. This work explores a novel approach to…
XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models
Erik Cambria, Lorenzo Malandri, Fabio Mercorio +2
In this survey, we address the key challenges in Large Language Models (LLM) research, focusing on the importance of interpretability. Driven by increasing interest from AI and bus…
Disce aut Deficere: Evaluating LLMs Proficiency on the INVALSI Italian Benchmark
Fabio Mercorio, Mario Mezzanzanica, Daniele Potertì +2
Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to generate and manipulate human language, highlighting their potential across various…