Explores the challenge of defining and reducing toxic content in large language models, discussing categorization and safety methods.
Explores visualizing hidden states in Transformer language models to understand their internal decision-making process during text generation.
Explores methods for controlling attributes like topic and style in neural text generation using decoding strategies, prompt design, and fine-tuning.
Explores interactive methods for interpreting transformer language models, focusing on input saliency and neuron activation analysis.
A technical overview of approaches for building open-domain question answering systems using pretrained language models and neural networks.
A visual guide explaining how GPT-3 is trained and generates text, breaking down its transformer architecture and massive scale.
An analysis of OpenAI's GPT-3 language model, focusing on its 175B parameters, in-context learning capabilities, and performance on NLP tasks.
A technical overview of the evolution of large-scale pre-trained language models like BERT, GPT, and T5, focusing on contextual embeddings and transfer learning in NLP.
A scatterplot analysis comparing perplexity results of Kneser-Ney, hierarchical Pitman-Yor, and hierarchical Dirichlet language models from a research paper.
