1 code implementation • CVPR 2023 • Roberto Dessì, Michele Bevilacqua, Eleonora Gualdoni, Nathanael Carraz Rakotonirina, Francesca Franzon, Marco Baroni
However, when the model is used without further tuning to generate captions for out-of-domain datasets, our discriminatively-finetuned captioner generates descriptions that resemble human references more than those produced by the same captioner without finetuning.
1 code implementation • 20 Feb 2023 • Nathanaël Carraz Rakotonirina, Roberto Dessì, Fabio Petroni, Sebastian Riedel, Marco Baroni
We study whether automatically-induced prompts that effectively extract information from a language model can also be used, out-of-the-box, to probe other language models for the same information.
1 code implementation • 15 Feb 2023 • Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, Edouard Grave, Yann Lecun, Thomas Scialom
This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools.
no code implementations • NeurIPS 2023 • Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, Thomas Scialom
Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale.
1 code implementation • 4 Feb 2023 • Matéo Mahaut, Francesca Franzon, Roberto Dessì, Marco Baroni
As a first step in this direction, we systematically explore the task of \textit{referential communication} in a community of heterogeneous state-of-the-art pre-trained visual networks, showing that they can develop, in a self-supervised way, a shared protocol to refer to a target object among a set of candidates.
1 code implementation • 20 Oct 2022 • Roberto Dessì, Eleonora Gualdoni, Francesca Franzon, Gemma Boleda, Marco Baroni
We compare the 0-shot performance of a neural caption-based image retriever when given as input either human-produced captions or captions generated by a neural captioner.
no code implementations • EMNLP (BlackboxNLP) 2021 • Rahma Chaabouni, Roberto Dessì, Eugene Kharitonov
We present several focused modifications of Transformer that greatly improve generalization capabilities on SCAN and select one that remains on par with a vanilla Transformer on a standard machine translation (MT) task.
1 code implementation • NeurIPS 2021 • Roberto Dessì, Eugene Kharitonov, Marco Baroni
As deep networks begin to be deployed as autonomous agents, the issue of how they can communicate with each other becomes important.
no code implementations • 5 Nov 2019 • Roberto Dessì, Diane Bouchacourt, Davide Crepaldi, Marco Baroni
Research in multi-agent cooperation has shown that artificial agents are able to learn to play a simple referential game while developing a shared lexicon.
no code implementations • ACL 2019 • Roberto Dessì, Marco Baroni
Lake and Baroni (2018) introduced the SCAN dataset probing the ability of seq2seq models to capture compositional generalizations, such as inferring the meaning of "jump around" 0-shot from the component words.
no code implementations • IWSLT (EMNLP) 2018 • Mattia Antonino Di Gangi, Roberto Dessì, Roldano Cattoni, Matteo Negri, Marco Turchi
This paper describes FBK's submission to the end-to-end English-German speech translation task at IWSLT 2018.