Papers by Gustavo Gonçalves
Understanding the Effect of Model Compression on Social Bias in Large Language Models (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) trained with self-supervision on vast corpora of web text fit to the social biases of that text, leading to representational harm. |
| Approach: | They propose to use quantization and knowledge distillation to reduce the computational burden of LLMs to mitigate the effects of inappropriate social biases learned during pretraining. |
| Outcome: | The proposed methods reduce the computational burden of large language models by reducing their size and complexity. |