Transformer-based Neural Networks and Practical Solutions
Enhancing Performance and Overcoming Shortcomings
Transformer-based neural networks have demonstrated the ability to handle various tasks such as text generation, editing, and question-answering. Larger models often show better performance, but they can also lead to challenges. Practical solutions to overcome these shortcomings include scaling laws, energy-based models, and Hopfield models.
Research and Experiments
Researchers from Central Research Institute, 2012 Laboratories Huawei Technologies Co., Ltd. introduced a theoretical framework focused on the memorization process and performance dynamics of transformer-based language models. They conducted experiments using GPT-2 and vanilla Transformer models to validate theoretical insights and optimize decision-making in model training.
Training and Results
A 12-layer transformer LM was trained using the GPT-2 small tokenizer and architecture on the OpenWebText dataset. Training with different amounts of data showed insights into over-fitting and model energy dynamics. The experiments provided important theoretical insights and practical implications for model training.
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