Next Token Prediction
Technique used in language modeling where the model predicts the following token based on the previous ones.
Next Token Prediction is a critical technique in AI-based Natural Language Processing (NLP), significantly used in language modeling tasks. The main objective is to predict the next word or token in a sentence given the previous words or tokens. This concept forms the foundation of several important applications such as autocomplete functions, chatbots, and even complex tasks like translations and sentiment analysis. The proficiency of an AI model in next token prediction can deeply influence its performance in these various NLP tasks. The prediction is usually accomplished using statistical methods or deep learning models like Recurrent Neural Networks (RNN), Transformers, or specifically, models like GPT (Generative Pre-trained Transformer) by OpenAI.
The technique of Next Token Prediction became prominent with the advent of modern NLP and deep learning methodologies. This technique started gaining popularity with the rise of deep learning architectures like RNN and LSTM (Long Short Term Memory) in the 1990s and early 2000s. It further gained significant attention with the introduction of transformers and language models like GPT in recent years.
Several individuals and organizations have contributed to the use and refinement of Next Token Prediction. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton have made significant contributions to deep learning techniques that underlie next token prediction. Organizations like Google, Facebook, and OpenAI have been instrumental in developing and popularizing transformer models like BERT (Bidirectional Encoder Representations from Transformers) and GPT which leverage next token prediction.