Papers by Vincent Micheli
On the importance of pre-training data volume for compact language models (2020.emnlp-main)
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| Challenge: | Recent advances in language modeling have led to computationally intensive and resource-demanding state-of-the-art models. |
| Approach: | They investigate the impact of pre-training data volume on compact language models . they use a French question answering task to train models with as little as 100 MB of text . |
| Outcome: | The results show that pre-training data volume can improve models with as little as 100 MB of text . the results suggest that the model performance is poorer with less data than with larger datasets . |
Language Models are Few-Shot Butlers (2021.emnlp-main)
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| Challenge: | Pretrained language models demonstrate strong performance in most NLP tasks when fine-tuned on small task-specific datasets. |
| Approach: | They propose a two-stage procedure to learn from a small set of demonstrations and a simple reinforcement learning algorithm to improve by interacting with an environment. |
| Outcome: | The proposed method improves with only 1.2% of the demonstrations and a simple reinforcement learning algorithm over existing methods in the ALFWorld environment. |