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pro vyhledávání: '"Rizzoli, Massimo"'
The automatic detection of temporal relations among events has been mainly investigated with encoder-only models such as RoBERTa. Large Language Models (LLM) have recently shown promising performance in temporal reasoning tasks such as temporal quest
Externí odkaz:
http://arxiv.org/abs/2410.10476
Autor:
Alghisi, Simone, Rizzoli, Massimo, Roccabruna, Gabriel, Mousavi, Seyed Mahed, Riccardi, Giuseppe
We study the limitations of Large Language Models (LLMs) for the task of response generation in human-machine dialogue. Several techniques have been proposed in the literature for different dialogue types (e.g., Open-Domain). However, the evaluations
Externí odkaz:
http://arxiv.org/abs/2406.06399
Autor:
Mousavi, Seyed Mahed, Roccabruna, Gabriel, Alghisi, Simone, Rizzoli, Massimo, Ravanelli, Mirco, Riccardi, Giuseppe
Large Pre-Trained Language Models have demonstrated state-of-the-art performance in different downstream tasks, including dialogue state tracking and end-to-end response generation. Nevertheless, most of the publicly available datasets and benchmarks
Externí odkaz:
http://arxiv.org/abs/2401.02297
Autor:
Zardini, Enrico, Rizzoli, Massimo, Dissegna, Sebastiano, Blanzieri, Enrico, Pastorello, Davide
Publikováno v:
Quantum Information and Computation 22 15-16 (2022) 1320-1350
Bayesian networks are widely used probabilistic graphical models, whose structure is hard to learn starting from the generated data. O'Gorman et al. have proposed an algorithm to encode this task, i.e., the Bayesian network structure learning (BSNL),
Externí odkaz:
http://arxiv.org/abs/2204.03526