Nearest Neighbor Language Model

Nearest neighbor language models (NNLMs) enhance traditional language models by incorporating a large external memory, retrieving similar past examples to aid in predicting the next word. Current research focuses on understanding the limitations of NNLMs, particularly their struggles with reasoning tasks despite strong memory capabilities, and on improving their adaptability to new domains and stylistic control through techniques like datastore augmentation and learned rescoring of retrieved neighbors. This approach offers a promising avenue for improving language model performance, particularly in applications requiring access to a vast knowledge base or stylistic control, but further investigation is needed to fully realize its potential.