Papers by Masoud Monajatipoor
MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models (2023.acl-short)
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| Challenge: | Large-scale pre-trained vision-language models do not possess the ability to conduct in-context learning. |
| Approach: | They propose to meta-train a language model to perform in-context learning on NLP tasks and then transfer this model to VL tasks by attaching a visual encoder. |
| Outcome: | The proposed model outperforms the baseline model on VQA, OK-VQA, and GQA while having 20 times fewer parameters. |
GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models (2022.emnlp-main)
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| Challenge: | Recent work shows that Pre-trained Language Models store relational knowledge and utilize it for performing downstream tasks. |
| Approach: | They propose a benchmark dataset for probing the diversity of relational knowledge in multilingual PLMs. |
| Outcome: | The proposed dataset contains 3125 prompts in English, Chinese, Hindi, Persian, and Swahili . larger multilingual PLMs variants do not store geo-diverse concepts better than its smaller variant . |
Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies (2021.emnlp-main)
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| Challenge: | Recent work analyzes, quantifies, and mitigates language model biases such as gender, race or religion-related stereotypes in static word embeddings and contextual representations. |
| Approach: | They explain the complexity of gender and language around it and examine how current representations perpetuate harms associated with binary gender. |
| Outcome: | The proposed model and dataset biases perpetuate harms associated with the treatment of gender as binary in English language technologies. |
Medical Vision-Language Pre-Training for Brain Abnormalities (2024.lrec-main)
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| Challenge: | Existing vision-language models lack expertise for medical applications due to the scarcity and complexity of data. |
| Approach: | They propose a pipeline to collect medical image-text aligned data for pretraining from public resources such as PubMed and build a high-performance vision-language model tailored to specific medical tasks. |
| Outcome: | The proposed model is based on a large brain image-text dataset and will be released to the public. |
How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions? (2022.emnlp-main)
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| Challenge: | Text-to-image generative models can generate high-quality photo-realistic images conditional on natural language text descriptions in a zero-shot fashion. |
| Approach: | They propose an Ethical NaTural Language Interventions in Text-to-Image GENeration benchmark dataset to evaluate the change in image generation conditional on ethical interventions across three social axes – gender, skin color, and culture. |
| Outcome: | The proposed model generations cover diverse social groups while preserving image quality. |