Zobrazeno 1 - 10
of 8 151
pro vyhledávání: '"GATT, A."'
In Natural Language Generation (NLG), important information is sometimes omitted in the output text. To better understand and analyse how this type of mistake arises, we focus on RDF-to-Text generation and explore two methods of probing omissions in
Externí odkaz:
http://arxiv.org/abs/2409.16707
This study investigates the ability of various vision-language (VL) models to ground context-dependent and non-context-dependent verb phrases. To do that, we introduce the CV-Probes dataset, designed explicitly for studying context understanding, con
Externí odkaz:
http://arxiv.org/abs/2409.01389
Due to their length and complexity, long regulatory texts are challenging to summarize. To address this, a multi-step extractive-abstractive architecture is proposed to handle lengthy regulatory documents more effectively. In this paper, we show that
Externí odkaz:
http://arxiv.org/abs/2408.09777
Autor:
Schmidtová, Patrícia, Mahamood, Saad, Balloccu, Simone, Dušek, Ondřej, Gatt, Albert, Gkatzia, Dimitra, Howcroft, David M., Plátek, Ondřej, Sivaprasad, Adarsa
Automatic metrics are extensively used to evaluate natural language processing systems. However, there has been increasing focus on how they are used and reported by practitioners within the field. In this paper, we have conducted a survey on the use
Externí odkaz:
http://arxiv.org/abs/2408.09169
Visual storytelling systems generate multi-sentence stories from image sequences. In this task, capturing contextual information and bridging visual variation bring additional challenges. We propose a simple yet effective framework that leverages the
Externí odkaz:
http://arxiv.org/abs/2408.06259
Various benchmarks have been proposed to test linguistic understanding in pre-trained vision \& language (VL) models. Here we build on the existence task from the VALSE benchmark (Parcalabescu et al, 2022) which we use to test models' understanding o
Externí odkaz:
http://arxiv.org/abs/2407.10488
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 LLMs instead of human judgments, raising questions about the validity of these evaluations, as well as their reproducibility in the case of proprietary models. We provide JUDGE-BENCH, an
Externí odkaz:
http://arxiv.org/abs/2406.18403
A Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences
The reasoning abilities of Large Language Models (LLMs) are becoming a central focus of study in NLP. In this paper, we consider the case of syllogistic reasoning, an area of deductive reasoning studied extensively in logic and cognitive psychology.
Externí odkaz:
http://arxiv.org/abs/2406.11341
Autor:
Grandis, S., Ghirardini, V., Bocquet, S., Garrel, C., Mohr, J. J., Liu, A., Kluge, M., Kimmig, L., Reiprich, T. H., Alarcon, A., Amon, A., Artis, E., Bahar, Y. E., Balzer, F., Bechtol, K., Becker, M. R., Bernstein, G., Bulbul, E., Campos, A., Rosell, A. Carnero, Kind, M. Carrasco, Cawthon, R., Chang, C., Chen, R., Chiu, I., Choi, A., Clerc, N., Comparat, J., Cordero, J., Davis, C., Derose, J., Diehl, H. T., Dodelson, S., Doux, C., Drlica-Wagner, A., Eckert, K., Elvin-Poole, J., Everett, S., Ferte, A., Gatt, M., Giannini, G., Giles, P., Gruen, D., Gruendl, R. A., Harrison, I., Hartley, W. G., Herner, K., Huf, E. M., Kleinebreil, F., Kuropatkin, N., Leget, P. F., Maccrann, N., Mccullough, J., Merloni, A., Myles, J., Nandra, K., Navarro-Alsina, A., Okabe, N., Pacaud, F., Pandey, S., Prat, J., Predehl, P., Ramos, M., Raveri, M., Rollins, R. P., Roodman, A., Ross, A. J., Rykoff, E. S., Sanchez, C., Sanders, J., Schrabback, T., Secco, L. F., Seppi, R., Sevilla-Noarbe, I., Sheldon, E., Shin, T., Troxel, M., Tutusaus, I., Varga, T. N., Wu, H., Yanny, B., Yin, B., Zhang, X., Zhang, Y., Alves, O., Bhargava, S., Brooks, D., Burke, D. L., Carretero, J., Costanzi, M., da Costa, L. N., Pereira, M. E. S., De Vicente, J., Desai, S., Doel, P., Ferrero, I., Flaugher, B., Friedel, D., Frieman, J., García-Bellido, J., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Jeffrey, N., Lahav, O., Lee, S., Marshall, J. L., Menanteau, F., Ogando, R. L. C., Pieres, A., Malagón, A. A. Plazas, Romer, A. K., Sanchez, E., Schubnell, M., Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., Weaverdyck, N., Weller, J.
Publikováno v:
A&A 687, A178 (2024)
Number counts of galaxy clusters across redshift are a powerful cosmological probe, if a precise and accurate reconstruction of the underlying mass distribution is performed -- a challenge called mass calibration. With the advent of wide and deep pho
Externí odkaz:
http://arxiv.org/abs/2402.08455
Autor:
Kesen, Ilker, Pedrotti, Andrea, Dogan, Mustafa, Cafagna, Michele, Acikgoz, Emre Can, Parcalabescu, Letitia, Calixto, Iacer, Frank, Anette, Gatt, Albert, Erdem, Aykut, Erdem, Erkut
With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present Vi
Externí odkaz:
http://arxiv.org/abs/2311.07022