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Long-context / context-window extension

H2O

H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models

Superseded baseline#9 of 53 most-superseded · first seen Jun 24, 2023

Superseded — cited as a baseline and beaten by newer methods

5 papers critique it · 3 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites H2O as a baseline.

Although these methods generally have low additional overhead, they often lead to noticeable performance degradation.
— A$^2$ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization
“While these methods differ in selecting tokens for KV cache retention, they generally apply a uniform budget size across layers, even though the optimal budget size may vary.”
— ZigZagkv: Dynamic KV Cache Compression for Long-context Modeling based on Layer Uncertainty
“H2O maintains a fixed-size KV cache during decoding by dynamically evicting tokens.”
— CSAttention: Centroid-Scoring Attention for Accelerating LLM Inference
“both rely on manually designed patterns or rules, which limits their ability to capture highly input-dependent attention sparsity”
— Long-Context Modeling with Dynamic Hierarchical Sparse Attention for On-Device LLMs
“H2O~zhang2024h2o reduces memory costs and achieves better accuracy than StreamingLLM, but its reliance on attention maps makes it incompatible with the efficient attention implementation FlashAttention~dao2023flashattention, leading to slow attention computation.”
— LaCache: Ladder-Shaped KV Caching for Efficient Long-Context Modeling of Large Language Models

Beaten on benchmarks

Head-to-head results where a newer method reports beating H2O. Values are copied from the source paper's tables — verify against the cited paper.

What to use instead

Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.