4 citations · 9 across the 7 of their papers we have counts for
8 papers
What do your logits know?
Masha Fedzechkina, Eleonora Gualdoni, Rita Ramos +1
Recent work has shown that probing model internals can reveal a wealth of information not apparent from the model generations. This poses a risk of unintentional or malicious infor…
CONCAP: Seeing Beyond English with Concepts Retrieval-Augmented Captioning
George Ibrahim, Rita Ramos, Yova Kementchedjhieva
Multilingual vision-language models have made significant strides in image captioning, yet they still lag behind their English counterparts due to limited multilingual training dat…
GrammaMT: Improving Machine Translation with Grammar-Informed In-Context Learning
Rita Ramos, Everlyn Asiko Chimoto, Maartje ter Hoeve +1
We introduce GrammaMT, a grammatically-aware prompting approach for machine translation that uses Interlinear Glossed Text (IGT), a common form of linguistic description providing…
Understanding Retrieval Robustness for Retrieval-Augmented Image Captioning
Wenyan Li, Jiaang Li, Rita Ramos +2
Recent advances in retrieval-augmented models for image captioning highlight the benefit of retrieving related captions for efficient, lightweight models with strong domain-transfe…
LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model Prompting
Rita Ramos, Bruno Martins, Desmond Elliott
Multilingual image captioning has recently been tackled by training with large-scale machine translated data, which is an expensive, noisy, and time-consuming process. Without requ…
Retrieval-augmented Image Captioning
Rita Ramos, Desmond Elliott, Bruno Martins
Inspired by retrieval-augmented language generation and pretrained Vision and Language (V&L) encoders, we present a new approach to image captioning that generates sentences given…
SmallCap: Lightweight Image Captioning Prompted with Retrieval Augmentation
Rita Ramos, Bruno Martins, Desmond Elliott +1
Recent advances in image captioning have focused on scaling the data and model size, substantially increasing the cost of pre-training and finetuning. As an alternative to large mo…
Retrieval Augmentation for Deep Neural Networks
Rita Parada Ramos, Patrícia Pereira, Helena Moniz +2
Deep neural networks have achieved state-of-the-art results in various vision and/or language tasks. Despite the use of large training datasets, most models are trained by iteratin…