What Kinds of Tokens Benefit from Distant Text? An Analysis on Long Context Language Modeling (2406.11238v1)

Abstract: As the context length that LLMs can handle continues to increase, these models demonstrate an enhanced ability to utilize distant information for tasks such as LLMing. This capability contrasts with human reading and writing habits, where it is uncommon to remember and use particularly distant information, except in cases of foreshadowing. In this paper, we aim to explore which kinds of words benefit more from long contexts in LLMs. By analyzing the changes in token probabilities with increasing context length, we find that content words (e.g., nouns, adjectives) and the initial tokens of words benefit the most. Frequent patterns in the context (N-grams) also significantly impact predictions. Additionally, the model's prior knowledge plays a crucial role in influencing predictions, especially for rare tokens. We also observe that LLMs become more confident with longer contexts, resulting in sharper probability distributions. This overconfidence may contribute to the increasing probabilities of tokens with distant contextual information. We hope that our analysis will help the community better understand long-text LLMing and contribute to the design of more reliable long-context models.