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20232025
most citedEmergent Abilities in Large Language Models: A Survey

4 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025★ 4 cited

Emergent Abilities in Large Language Models: A Survey

Leonardo Berti, Flavio Giorgi, Gjergji Kasneci

Large Language Models (LLMs) are leading a new technological revolution as one of the most promising research streams toward artificial general intelligence. The scaling of these m…

q-fin.ST2025

TLOB: A Novel Transformer Model with Dual Attention for Price Trend Prediction with Limit Order Book Data

Leonardo Berti, Gjergji Kasneci

Price Trend Prediction (PTP) based on Limit Order Book (LOB) data is a fundamental challenge in financial markets. Despite advances in deep learning, existing models fail to genera…

q-fin.TR2025★ 1 cited

TRADES: Generating Realistic Market Simulations with Diffusion Models

Leonardo Berti, Bardh Prenkaj, Paola Velardi

Financial markets are complex systems characterized by high statistical noise, nonlinearity, volatility, and constant evolution. Thus, modeling them is extremely hard. Here, we add…

cs.SD2024

Source Separation of Multi-source Raw Music using a Residual Quantized Variational Autoencoder

Leonardo Berti

I developed a neural audio codec model based on the residual quantized variational autoencoder architecture. I train the model on the Slakh2100 dataset, a standard dataset for musi…

q-fin.TR2023★ 1 cited

LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study

Matteo Prata, Giuseppe Masi, Leonardo Berti +6

The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL mod…