Summary of Scbench: a Kv Cache-centric Analysis Of Long-context Methods, by Yucheng Li et al.
SCBench: A KV Cache-Centric Analysis of Long-Context Methods
by Yucheng Li, Huiqiang Jiang, Qianhui Wu, Xufang Luo, Surin Ahn, Chengruidong Zhang, Amir H. Abdi, Dongsheng Li, Jianfeng Gao, Yuqing Yang, Lili Qiu
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the challenges of computational and memory efficiency in Long-Context Large Language Models (LLMs) by introducing a new benchmark called SCBench. SCBench evaluates long-context methods from a KV cache-centric perspective, considering four categories of capabilities: string retrieval, semantic retrieval, global information, and multi-task. The evaluation is conducted on eight long-context LLMs, showing that sub-O(n) memory methods suffer in multi-turn scenarios, while sparse encoding with O(n) memory performs robustly. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a benchmark for evaluating Long-Context Large Language Models (LLMs). It focuses on the KV cache and how it affects the performance of these models. The authors tested eight different LLMs and found that some methods are better than others in certain situations. This could help improve the way we use LLMs in the future. |
Keywords
* Artificial intelligence * Multi task
