3 papers
cs.DB2026
Can we trust LLM Self-Explanations for Entity Resolution?
Tommaso Teofili, Donatella Firmani, Nick Koudas +2
Large Language Models (LLMs) have recently shown strong performance on Entity Resolution (ER). Additionally, akin to their prowess in providing accurate predictions, these models o…
cs.SD2026
Towards Realistic Synthetic Data for Automatic Drum Transcription
Pierfrancesco Melucci, Paolo Merialdo, Taketo Akama
Deep learning models define the state-of-the-art in Automatic Drum Transcription (ADT), yet their performance is contingent upon large-scale, paired audio-MIDI datasets, which are…
cs.CL2025
How to Connect Speech Foundation Models and Large Language Models? What Matters and What Does Not
Francesco Verdini, Pierfrancesco Melucci, Stefano Perna +9
The remarkable performance achieved by Large Language Models (LLM) has driven research efforts to leverage them for a wide range of tasks and input modalities. In speech-to-text (S…