no code implementations • 4 Apr 2024 • Marco Bronzini, Carlo Nicolini, Bruno Lepri, Jacopo Staiano, Andrea Passerini
We propose an end-to-end framework that jointly decodes the factual knowledge embedded in the latent space of LLMs from a vector space to a set of ground predicates and represents its evolution across the layers using a temporal knowledge graph.
no code implementations • 13 Mar 2024 • Carlo Nicolini, Jacopo Staiano, Bruno Lepri, Raffaele Marino
A substantial gap persists in understanding the reasons behind the exceptional performance of the Transformer architecture in NLP.
1 code implementation • 9 Oct 2023 • Marco Bronzini, Carlo Nicolini, Bruno Lepri, Andrea Passerini, Jacopo Staiano
This poses a challenge in efficiently gathering and aligning the data into a unified framework to derive insights related to Corporate Social Responsibility (CSR).