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Skip to Search Results- 2Abdi Oskouie, Mina
- 2Birkbeck, Neil Aylon Charles
- 2Cai, Zhipeng
- 2Chen, Jiyang
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 83Machine Learning
- 76Reinforcement Learning
- 42Artificial Intelligence
- 37Machine learning
- 24Natural Language Processing
- 23reinforcement learning
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Fall 2024
It has been shown that pretrained language models exhibit biases and social stereotypes. Prior work on debiasing these language models has largely focussed on modifying embedding spaces in pretraining, which is not scalable for large models. Since pretrained models are typically fine-tuned on...
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Budgeted Gradient Descent: Selective Gradient Optimization for Addressing Misclassifications in DNNs
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Artificial neural networks have become a popular learning approach for theirability to generalize well to unseen data. However, misclassifications can still occur due to various data-related issues, such as adversarial inputs, out-of-distribution samples, and model-related challenges, such as...
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Fall 2024
Training large language models (LLMs) often requires extensive human supervision and struggles with modeling long-range text semantic dependencies. To address these challenges, we introduce our framework ELITE — Evolving Language models Iteratively Through self-critiquE — inspired by human...