Ref: https://arxiv.org/abs/1901.02860

The

Ref: https://arxiv.org/abs/2005.14165

This research paper introduces GPT-3, a large language model developed by OpenAI and Johns Hopkins University. The paper details GPT-3's architecture, training data, and performance across numerous natural language processing tasks, focusing on its ability to perform well in zero-shot, one-shot, and few-shot learning settings. Results show GPT-3 achieves state-of-the-art performance on some tasks, though limitations such as biases and potential for misuse are also addressed. The authors analyze data contamination issues and explore GPT-3's capabilities in tasks involving reasoning and novel word usage. Finally, the study concludes with a discussion of the broader societal implications of such powerful language models.

ref: https://arxiv.org/abs/1910.10683

This research paper introduces T5, a text-to-text transfer transformer model that achieves state-of-the-art results on various natural language processing benchmarks. The authors present a unified framework converting diverse NLP tasks into a text-to-text format, enabling systematic comparison of different transfer learning techniques. A new large-scale dataset, the Colossal Clean Crawled Corpus (C4), is introduced and released, along with pre-trained models and code. The study explores the effects of different pre-training objectives, architectures, and data sets on model performance, demonstrating the significant impact of scale on results. Finally, the authors discuss their findings and suggest avenues for future research in this area.

Ref: https://arxiv.org/abs/1810.04805

This research paper introduces BERT, a novel language representation model using bidirectional Transformer encoders. Unlike previous unidirectional models, BERT pre-trains deep bidirectional representations by jointly conditioning on both left and right context. This allows for state-of-the-art performance on various natural language processing tasks after fine-tuning with a single output layer. The authors present extensive experiments demonstrating BERT's superior performance and conduct ablation studies to analyze the impact of different model components and pre-training strategies. Finally, they compare the fine-tuning approach with a feature-based approach, showing BERT's effectiveness in both.

Ref: https://openai.com/index/language-unsupervised/

This research paper explores a semi-supervised approach to improving language understanding using a two-stage process. First, a large language model is pre-trained on a massive unlabeled text corpus. Second, this pre-trained model is fine-tuned on various downstream tasks using task-aware input transformations. The authors demonstrate significant performance improvements across multiple natural language understanding benchmarks, outperforming previous state-of-the-art models in nine out of twelve tasks. This success is attributed to the model's ability to learn robust representations from extensive unsupervised pre-training and its adaptability to different tasks with minimal architectural changes. The study also investigates the impact of the number of transferred layers and zero-shot behaviors.

Ref: https://arxiv.org/abs/1706.03762

This classic research paper introduces the Transformer, a novel neural network architecture for sequence transduction tasks like machine translation. Unlike previous models relying on recurrent or convolutional layers, the Transformer uses solely attention mechanisms, enabling greater parallelization and faster training. Experiments demonstrate its superior performance on English-to-German and English-to-French translation, achieving state-of-the-art results with significantly reduced training costs. Furthermore, the Transformer's effectiveness extends to other tasks, as shown by its successful application to English constituency parsing. The paper details the Transformer's architecture, including multi-head attention and positional encoding, and analyzes its advantages over existing methods.