Taking a Deep Breath: Enhancing Language Modeling of Large Language Models with Sentinel Tokens

TL;DR

This paper introduces a method to improve large language models' ability to handle long contexts by using sentinel tokens to summarize and integrate chunked text information, enhancing performance on language modeling and downstream tasks.

Contribution

The paper proposes a novel chunking and sentinel token mechanism that enables LLMs to better capture long-term dependencies without increasing computational costs.

Findings

Improved language modeling performance on long texts

Enhanced downstream task accuracy

Effective chunk summarization with sentinel tokens

Abstract

Large language models (LLMs) have shown promising efficacy across various tasks, becoming powerful tools in numerous aspects of human life. However, Transformer-based LLMs suffer a performance degradation when modeling long-term contexts due to they discard some information to reduce computational overhead. In this work, we propose a simple yet effective method to enable LLMs to take a deep breath, encouraging them to summarize information contained within discrete text chunks. Specifically, we segment the text into multiple chunks and insert special token <SR> at the end of each chunk. We then modify the attention mask to integrate the chunk's information into the corresponding <SR> token. This facilitates LLMs to interpret information not only from historical individual tokens but also from the <SR> token, aggregating the chunk's semantic information. Experiments on language modeling and out-of-domain downstream tasks validate the superiority of our approach.