Long text sequences pose challenges in NLP because traditional models like RNNs and LSTMs struggle with retaining context over extended inputs. As text length increases, these models often lose track of earlier information, leading to diminished performance in tasks requiring a holistic understanding of the text.
Transformer models like BERT and GPT address this issue using self-attention mechanisms, which allow them to focus on all parts of a sequence simultaneously. However, transformers have their limitations, as their computational and memory requirements scale quadratically with sequence length. To mitigate this, techniques like positional encoding and segment embeddings are used to capture context more efficiently.
For extremely long documents, models like Longformer and BigBird modify the attention mechanism to handle longer sequences while maintaining computational efficiency. Splitting text into manageable chunks and processing them separately, then aggregating results, is another common strategy. Despite these advancements, effectively modeling long sequences without losing context remains a computational and architectural challenge in NLP.