Papers by Rita Ramos
Retrieval-augmented Image Captioning (2023.eacl-main)
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| Challenge: | Experimental results show that image captioning can be effectively formulated from this new perspective. |
| Approach: | They propose a pretrained visual and language decoders for image captioning that generate sentences from the input image and a set of captions retrieved from a datastore. |
| Outcome: | The proposed model generates sentences given the input image and retrieved captions, while the decoder attends to the multimodal encoder representations. |
LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model Prompting (2023.findings-acl)
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| Challenge: | Existing captioning models are mostly available on English or are specialised for other high-resource languages. |
| Approach: | They propose an image-blind few-shot multilingual captioning model that prompts a language model with retrieved captions. |
| Outcome: | The proposed model is competitive with fully-supervised multilingual captioning models without requiring any supervised training on captioning data. |
PAELLA: Parameter-Efficient Lightweight Language-Agnostic Captioning Model (2024.findings-naacl)
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| Challenge: | Existing models that only generate English captions are expensive due to the trend of scaling both data and model size. |
| Approach: | They propose a parameter-efficient lightweight language-agnostic captioning model that uses retrieval enhancement to train parameters between a visual model and a multilingual language model. |
| Outcome: | The proposed model outperforms models with more parameters and data and shows strong zero-shot abilities in low-resource languages. |
Understanding Retrieval Robustness for Retrieval-augmented Image Captioning (2024.acl-long)
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| Challenge: | Recent retrieval-augmented models for image captioning are not perfect in practice. |
| Approach: | They propose to train a retrieval-augmented captioning model SmallCap by sampling retrieved captions from more diverse sets. |
| Outcome: | The proposed model is sensitive to tokens that appear in the majority of retrieved captions . the proposed model improves both in-domain and cross-domain performance . |
GrammaMT: Improving Machine Translation with Grammar-Informed In-Context Learning (2025.acl-long)
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| Challenge: | Experiments show that GrammaMT enhances translation performance on open-source instruction-tuned LLMs for various low- to high-resource languages across three benchmarks: (1) largest corpus, (2) challenging 2023 SIGMORPHON Shared Task data, (3) even in an out-of-domain setting with FLORES. |
| Approach: | They propose a grammatically-aware prompting approach that uses Interlinear Glossed Text . they propose gloss-shot, chain-gloss and model-glooss prompting strategies that are training-free . |
| Outcome: | Experiments show that GrammaMT improves translation performance on open-source instruction-tuned LLMs for various low- to high-resource languages across three benchmarks. |