Managing token limits in large language models (LLMs) like GPT-4 requires advanced techniques to ensure essential information is preserved and responses remain coherent. Key strategies include:

1. Dynamic Token Management: Use a sliding window approach and priority scoring to adjust context dynamically based on relevance and importance.
2. Context-Aware Truncation: Implement semantic chunking and topic segmentation to truncate text at natural boundaries, maintaining coherence and context.
3. Selective Information Prioritization: Prioritize entities and key terms using entity recognition and dependency parsing to retain critical information.
4. Advanced Compression Algorithms: Employ knowledge distillation, selective attention mechanisms, and contextual embeddings to compress information effectively.
5. Hierarchical Summarization: Perform summarization at multiple levels, summarizing sections first (micro) and then the entire document (macro).
6. Reinforcement Learning: Utilize reward-based truncation and adaptive context length to train models for efficient token management.
7. Novel Algorithms: Explore sparse attention models and neural topic models to handle longer sequences and focus on relevant tokens.

Implementing these strategies, including preprocessing, chunking, contextual understanding, and fine-tuning, optimizes token usage, preserving essential information and ensuring coherent responses in LLMs.