2 citations · 5 across the 5 of their papers we have counts for
5 papers
Evil twins are not that evil: Qualitative insights into machine-generated prompts
Nathanaël Carraz Rakotonirina, Corentin Kervadec, Francesca Franzon +1
It has been widely observed that language models (LMs) respond in predictable ways to algorithmically generated prompts that are seemingly unintelligible. This is both a sign that…
Unnatural language processing: How do language models handle machine-generated prompts?
Corentin Kervadec, Francesca Franzon, Marco Baroni
Language model prompt optimization research has shown that semantically and grammatically well-formed manually crafted prompts are routinely outperformed by automatically generated…
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…
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…
Communication breakdown: On the low mutual intelligibility between human and neural captioning
Roberto Dessì, Eleonora Gualdoni, Francesca Franzon +2
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. We condu…