SCBench

[Paper] [Code]

SCBench (SharedContextBench) is a comprehensive benchmark to evaluate efficient long-context methods in a KV cache-centric perspective, analyzing their performance across the full KV cache lifecycle (generation, compression, retrieval, and loading) in real-world scenarios where context memory (KV cache) is shared and reused across multiple requests.

Dataset

SCBench covers 12 diverse tasks that test four key long-context capabilities: string retrieval, semantic retrieval, global information processing, and multi-tasking.

String Retrieval

• Retr.KV: Tests key-value lookup in large JSON objects with random, incompressible content
• Retr.Prefix-Suffix: Evaluates finding strings with specific prefix and suffix patterns
• Retr.MultiHop: Assesses multi-hop variable tracing capabilities in long inputs

Semantic Retrieval

• Code.RepoQA: Function retrieval from large codebases based on natural language descriptions
• Language QA: Includes English QA, Chinese QA, and multi-choice questions on long texts
• Requires semantic understanding on length inputs

Global Information Processing

• Many-shot ICL: Tests in-context learning with hundreds of examples
• Math.Find: Statistical tasks on large arrays
• En.Sum: Summarization of documents
• Requires global information processing or aggregation

Multi-Tasking

• Mix.Sum+NIAH: Combines summarization with needle-in-haystack search
• Mix.RepoQA+KV: Integrates code function retrieval with key-value lookup
• Requires multi-tasking or multi-step reasoning

Two Shared Context Modes

The benchmark evaluates these tasks across two shared context modes:

• Multi-turn Mode: Caches context within single sessions
• Multi-request Mode: Shares context across multiple sessions

Compared to previous long-context benchmarks

Our SCBench is the first long-context benchmark that covers single-turn, multi-turn, and multi-request scenarios. In addition, our impelmentation also involves KV cache reuse techniques, thereby providing a more comprehensive analysis on the full KV cache lifecycle of efficient long-context methods.

Results and Findings

Our SCBench reveals that the following key insights:

Finding 1: Sub-O(n) Memory is Problematic in Multi-Request/Multi-Turn Decoding

• Sparse decoding methods with sub-O(n) memory perform well on first queries but lose accuracy in subsequent requests
• Methods maintaining O(n) memory with sub-O(n²) computation during pre-filling can better approximate full attention accuracy across multiple queries

Finding 2: Task Performance Shows Varying Decline Patterns

• Sparse KV cache methods excel in tasks requiring global information processing
• O(n) memory is essential for tasks involving exact match retrieval

Finding 3: Performance vs Compression Rate

• All methods show performance degradation as compression rates increase
• Sub-O(n) memory methods exhibit significant drop at 1/4 compression rate
• Methods like RetrievalAttention and KIVI that maintain O(n) memory with sparse decoding show better resilience at higher compression rates

Finding 4: Issues with Long-Generation Scenarios

• Attention distribution shifts significantly as generation length and number of rounds increase
• This out-of-distribution (OOD) issue impacts performance even for O(n) memory methods

Finding 5: Dynamic vs Static Patterns

• Dynamic sparse patterns generally outperform static patterns

Citation

@article{li2024scbench,
    title={SCBench: A KV cache-centric analysis of long-context methods},
    author={Li, Yucheng and Jiang, Huiqiang and Wu, Qianhui and Luo, Xufang and Ahn, Surin and Zhang, Chengruidong and Abdi, Amir H and Li, Dongsheng and Gao, Jianfeng and Yang, Yuqing and Qiu, Lili},
    journal={arXiv preprint arXiv:2412.},
    year={2024}
}