400 citations · 566 across the 11 of their papers we have counts for
5 papers · 1 filter
Cross-Domain Image Captioning with Discriminative Finetuning
Roberto Dessì, Michele Bevilacqua, Eleonora Gualdoni +3
Neural captioners are typically trained to mimic human-generated references without optimizing for any specific communication goal, leading to problems such as the generation of va…
Can discrete information extraction prompts generalize across language models?
Nathanaël Carraz Rakotonirina, Roberto Dessì, Fabio Petroni +2
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 s…
Augmented Language Models: a Survey
Grégoire Mialon, Roberto Dessì, Maria Lomeli +10
This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools. The former is defined as decomposing a potentially comple…
Toolformer: Language Models Can Teach Themselves to Use Tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì +5
Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with…
Referential communication in heterogeneous communities of pre-trained visual deep networks
Matéo Mahaut, Francesca Franzon, Roberto Dessì +1
As large pre-trained image-processing neural networks are being embedded in autonomous agents such as self-driving cars or robots, the question arises of how such systems can commu…