Papers by Goncalo Mordido
Exploring Quantization for Efficient Pre-Training of Transformer Language Models (2024.findings-emnlp)
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| Challenge: | Quantization has proven to be effective after pre-training and during fine-tuning, but its effects on pre-trainer performance have remained unexplored. |
| Approach: | They propose a linear quantization strategy to be applied during the pre-training of Transformers to improve model efficiency and stability. |
| Outcome: | The proposed method improves model efficiency, stability, and performance while maintaining language modeling ability. |
Best Student Forcing: A Simple Training Mechanism in Adversarial Language Generation (2020.lrec-1)
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| Challenge: | Language models trained with Maximum Likelihood Estimation (MLE) have been considered as a mainstream solution in Natural Language Generation (NLG) however, they are reportedly suffering from training instability and mode collapse, and therefore outperform conventional MLE models. |
| Approach: | They propose a method to improve Generative Adversarial Nets (GANs) using best student forcing and discriminators to increase training stability and sample diversity. |
| Outcome: | The proposed techniques outperform MLE models and outperformed existing approaches in terms of sample diversity and training stability. |
Why Don’t Prompt-Based Fairness Metrics Correlate? (2024.acl-long)
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| Challenge: | Existing methods to assess fairness using prompts have low correlations between fairness metrics. |
| Approach: | They propose a method to enhance the correlation between fairness metrics by using pre-trained language models. |
| Outcome: | The proposed method improves the correlation between fairness metrics by using pre-trained language models. |