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pro vyhledávání: '"André, F."'
Recent studies have highlighted the potential of exploiting parallel corpora to enhance multilingual large language models, improving performance in both bilingual tasks, e.g., machine translation, and general-purpose tasks, e.g., text classification
Externí odkaz:
http://arxiv.org/abs/2407.00436
Autor:
Treviso, Marcos, Guerreiro, Nuno M., Agrawal, Sweta, Rei, Ricardo, Pombal, José, Vaz, Tania, Wu, Helena, Silva, Beatriz, van Stigt, Daan, Martins, André F. T.
While machine translation (MT) systems are achieving increasingly strong performance on benchmarks, they often produce translations with errors and anomalies. Understanding these errors can potentially help improve the translation quality and user ex
Externí odkaz:
http://arxiv.org/abs/2406.19482
Autor:
Bavaresco, Anna, Bernardi, Raffaella, Bertolazzi, Leonardo, Elliott, Desmond, Fernández, Raquel, Gatt, Albert, Ghaleb, Esam, Giulianelli, Mario, Hanna, Michael, Koller, Alexander, Martins, André F. T., Mondorf, Philipp, Neplenbroek, Vera, Pezzelle, Sandro, Plank, Barbara, Schlangen, David, Suglia, Alessandro, Surikuchi, Aditya K, Takmaz, Ece, Testoni, Alberto
There is an increasing trend towards evaluating NLP models with LLM-generated judgments instead of human judgments. In the absence of a comparison against human data, this raises concerns about the validity of these evaluations; in case they are cond
Externí odkaz:
http://arxiv.org/abs/2406.18403
Autor:
Faria, Gonçalo R. A., Agrawal, Sweta, Farinhas, António, Rei, Ricardo, de Souza, José G. C., Martins, André F. T.
An important challenge in machine translation (MT) is to generate high-quality and diverse translations. Prior work has shown that the estimated likelihood from the MT model correlates poorly with translation quality. In contrast, quality evaluation
Externí odkaz:
http://arxiv.org/abs/2406.00049
Automatic metrics for evaluating translation quality are typically validated by measuring how well they correlate with human assessments. However, correlation methods tend to capture only the ability of metrics to differentiate between good and bad s
Externí odkaz:
http://arxiv.org/abs/2405.18348
Recent studies indicate that leveraging off-the-shelf or fine-tuned retrievers, capable of retrieving relevant in-context examples tailored to the input query, enhances few-shot in-context learning of English. However, adapting these methods to other
Externí odkaz:
http://arxiv.org/abs/2405.05116
Autor:
Campos, Margarida M., Farinhas, António, Zerva, Chrysoula, Figueiredo, Mário A. T., Martins, André F. T.
The rapid proliferation of large language models and natural language processing (NLP) applications creates a crucial need for uncertainty quantification to mitigate risks such as hallucinations and to enhance decision-making reliability in critical
Externí odkaz:
http://arxiv.org/abs/2405.01976
The widespread usage of point clouds (PC) for immersive visual applications has resulted in the use of very heterogeneous receiving conditions and devices, notably in terms of network, hardware, and display capabilities. In this scenario, quality sca
Externí odkaz:
http://arxiv.org/abs/2404.07698
The partial information decomposition (PID) framework is concerned with decomposing the information that a set of (two or more) random variables (the sources) has about another variable (the target) into three types of information: unique, redundant,
Externí odkaz:
http://arxiv.org/abs/2403.16575
Autor:
Ferreira, Gabriel O., Zanella, André F., Bakirtzis, Stefanos, Ravazzi, Chiara, Dabbene, Fabrizio, Calafiore, Giuseppe C., Wassel, Ian, Zhang, Jie, Fiore, Marco
Heterogeneous networks have emerged as a popular solution for accommodating the growing number of connected devices and increasing traffic demands in cellular networks. While offering broader coverage, higher capacity, and lower latency, the escalati
Externí odkaz:
http://arxiv.org/abs/2403.14555