Transformer和LLM前沿内容(4):Long-Context LLM
2026/4/27 4:16:42 网站建设 项目流程


文章目录

      • 1. Context Extension
        • 1.1 Rotary Position Embedding (RoPE)
        • 1.2 LongLoRA
      • 2. Evaluation of Long-Context LLMs
        • 2.1 The Lost in the Middle Phenomenon
        • 2.2 Long-Context Benchmarks: NIAH, LongBench
      • 3. Efficient Attention Mechanisms
        • 3.1 KV Cache
        • 3.2 StreamingLLM and Attention Sinks(重点)
        • 3.3 DuoAttention: Retrieval Heads and Streaming Heads (重点)
        • 3.4 Quest: Query-Aware Sparsity(重点)
      • 4. Beyond Transformers
        • 4.1 State-Space Models (SSMs): Mamba
        • 4.2 Hybrid Models: Jamba

1. Context Extension

1.1 Rotary Position Embedding (RoPE)


1.2 LongLoRA




2. Evaluation of Long-Context LLMs

2.1 The Lost in the Middle Phenomenon

2.2 Long-Context Benchmarks: NIAH, LongBench



3. Efficient Attention Mechanisms

3.1 KV Cache


3.2 StreamingLLM and Attention Sinks(重点)














3.3 DuoAttention: Retrieval Heads and Streaming Heads (重点)









3.4 Quest: Query-Aware Sparsity(重点)









4. Beyond Transformers

4.1 State-Space Models (SSMs): Mamba





4.2 Hybrid Models: Jamba




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