Long-context evaluation asks whether a model can process, retrieve, and use distant information. An SSM specifies sequence processing through state updates. SSM efficiency does not establish reliable document retrieval, and a large attention window does not guarantee relevant evidence is used.
Lecture 15 has four parts. Extending context: interpolating RoPE stretches LLaMA from 2k to 32k, and LongLoRA's shifted sparse attention makes long-context fine-tuning cheap. Evaluation: lost-in-the-middle, Needle-in-a-Haystack, and LongBench. Efficient attention: the KV cache grows linearly with length. StreamingLLM finds that the first few tokens act as attention sinks, and keeping them plus a recent window gives stable generation. DuoAttention keeps a full KV cache only for a few retrieval heads. Quest keeps the whole KV cache but reads only the most critical pages for each query. The last part moves beyond Transformers: Mamba replaces attention with a selective SSM, and Jamba mixes the two.
Lecture 4 studies two kinds of sparsity: linear/recurrent attention reduces sequence-length cost, while MoE activates only part of a model for each token. Both turn saved FLOPs into routing, balancing, communication, and kernel problems.