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

• URL: http://arxiv.org/abs/2406.10985v1
• Date: Sun, 16 Jun 2024 15:50:10 GMT
• Title: Taking a Deep Breath: Enhancing Language Modeling of Large Language Models with Sentinel Tokens
• Authors: Weiyao Luo, Suncong Zheng, Heming Xia, Weikang Wang, Yan Lei, Tianyu Liu, Shuang Chen, Zhifang Sui,
• Abstract summary: Transformer-based large language models (LLMs) suffer a performance degradation when modeling long-term contexts. 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.
• Score: 21.61634020256455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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