Context Bigram

Context bigrams, pairs of consecutive words in text, are crucial for understanding how language models process and generate text. Current research focuses on leveraging bigram statistics to improve model performance, particularly through methods like contrastive learning and the development of "statistical induction heads" within transformer architectures. This work aims to enhance the quality of training data, improve the accuracy of semantic textual relatedness tasks across multiple languages, and ultimately lead to more reliable and efficient large language models.