How Large Language Models Handle Long Text and Long-Sequence Data

This article explores the core challenges and techniques used by modern large language models (LLMs) to handle long text and long-sequence data, such as long articles, legal contracts, and large codebases.

Details

Large language models (LLMs) are powerful at understanding and generating text, but they were not originally designed to handle very long documents. In real-world applications, models often need to process long articles, legal contracts, chat histories, logs, and large codebases. This raises the challenge of how LLMs can effectively handle long text and long-sequence data. The core issue is that the self-attention mechanism in transformers scales quadratically with sequence length, leading to high computational cost, GPU memory constraints, and increased latency. Early transformer models were limited to 512 or 1-2k tokens, but modern applications often require tens or hundreds of thousands of tokens. To address this, researchers have developed various techniques, including increasing context window size, improving positional encoding, attention optimization (sparse, sliding window, linear), chunking and hierarchical processing, retrieval-augmented generation (RAG), and memory/state-based methods. Production systems often combine multiple approaches to balance cost, accuracy, and flexibility. While longer context does not equate to perfect long-term memory, these techniques enable LLMs to effectively handle long-sequence data in real-world applications.