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README.md
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---
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license: mit
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dataset_info:
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- config_name: multi_turn_choice_eng
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configs:
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- config_name: multi_turn_choice_eng
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data_files:
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- split: train
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path: multi_turn_choice_eng/train-*
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- config_name: multi_turn_kv
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data_files:
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- split: train
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path: multi_turn_kv/train-*
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data_files:
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- split: train
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path: multi_turn_many_shot/train-*
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data_files:
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- split: train
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path: multi_turn_mf/train-*
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- config_name: multi_turn_prefix_suffix
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data_files:
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- split: train
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path: multi_turn_prefix_suffix/train-*
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- config_name: multi_turn_qa_chn
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data_files:
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- split: train
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path: multi_turn_qa_chn/train-*
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- config_name: multi_turn_qa_eng
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data_files:
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- split: train
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path: multi_turn_qa_eng/train-*
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data_files:
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- split: train
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path: multi_turn_repoqa/train-*
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- config_name: multi_turn_repoqa_and_kv
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path: multi_turn_repoqa_and_kv/train-*
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path: multi_turn_summary/train-*
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- config_name: multi_turn_summary_with_needles
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data_files:
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- split: train
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path: multi_turn_summary_with_needles/train-*
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data_files:
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- split: train
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path: multi_turn_vt/train-*
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---
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1 |
+
---
|
2 |
+
license: mit
|
3 |
+
dataset_info:
|
4 |
+
- config_name: multi_turn_choice_eng
|
5 |
+
features:
|
6 |
+
- name: context
|
7 |
+
dtype: string
|
8 |
+
- name: multi_turns
|
9 |
+
list:
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dtype: string
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splits:
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num_bytes: 46482955
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24 |
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25 |
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26 |
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27 |
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num_examples: 100
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40 |
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41 |
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dataset_size: 20071200
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42 |
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- config_name: multi_turn_many_shot
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43 |
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dataset_size: 58359967
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download_size: 4427455
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dataset_size: 24847710
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- config_name: multi_turn_repoqa_and_kv
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dataset_size: 25019328
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- config_name: multi_turn_summary
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features:
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- name: context
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dataset_size: 28622955
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- config_name: multi_turn_summary_with_needles
|
249 |
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features:
|
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- name: context
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dataset_size: 28629718
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|
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|
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configs:
|
289 |
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- config_name: multi_turn_choice_eng
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data_files:
|
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|
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path: multi_turn_choice_eng/train-*
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|
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- split: train
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path: multi_turn_kv/train-*
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- split: train
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path: multi_turn_mf/train-*
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- split: train
|
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path: multi_turn_prefix_suffix/train-*
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- config_name: multi_turn_qa_chn
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path: multi_turn_qa_chn/train-*
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- split: train
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path: multi_turn_qa_eng/train-*
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- config_name: multi_turn_repoqa
|
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data_files:
|
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- split: train
|
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path: multi_turn_repoqa/train-*
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- config_name: multi_turn_repoqa_and_kv
|
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data_files:
|
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- split: train
|
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path: multi_turn_repoqa_and_kv/train-*
|
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- config_name: multi_turn_summary
|
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data_files:
|
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- split: train
|
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path: multi_turn_summary/train-*
|
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- config_name: multi_turn_summary_with_needles
|
330 |
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data_files:
|
331 |
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- split: train
|
332 |
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path: multi_turn_summary_with_needles/train-*
|
333 |
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- config_name: multi_turn_vt
|
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data_files:
|
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- split: train
|
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path: multi_turn_vt/train-*
|
337 |
+
---
|
338 |
+
|
339 |
+
# SCBench
|
340 |
+
|
341 |
+
[[Paper]]()
|
342 |
+
[[Code]](https://github.com/microsoft/MInference/SCBench)
|
343 |
+
|
344 |
+
![SCBench](./data/framework.png)
|
345 |
+
|
346 |
+
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.
|
347 |
+
|
348 |
+
## Dataset
|
349 |
+
|
350 |
+
![SCBench](./data/overview.png)
|
351 |
+
|
352 |
+
SCBench covers 12 diverse tasks that test four key long-context capabilities: string retrieval, semantic retrieval, global information processing, and multi-tasking.
|
353 |
+
|
354 |
+
### String Retrieval
|
355 |
+
- **Retr.KV**: Tests key-value lookup in large JSON objects with random, incompressible content
|
356 |
+
- **Retr.Prefix-Suffix**: Evaluates finding strings with specific prefix and suffix patterns
|
357 |
+
- **Retr.MultiHop**: Assesses multi-hop variable tracing capabilities in long inputs
|
358 |
+
|
359 |
+
### Semantic Retrieval
|
360 |
+
- **Code.RepoQA**: Function retrieval from large codebases based on natural language descriptions
|
361 |
+
- **Language QA**: Includes English QA, Chinese QA, and multi-choice questions on long texts
|
362 |
+
- Requires semantic understanding on length inputs
|
363 |
+
|
364 |
+
### Global Information Processing
|
365 |
+
- **Many-shot ICL**: Tests in-context learning with hundreds of examples
|
366 |
+
- **Math.Find**: Statistical tasks on large arrays
|
367 |
+
- **En.Sum**: Summarization of documents
|
368 |
+
- Requires global information processing or aggregation
|
369 |
+
|
370 |
+
### Multi-Tasking
|
371 |
+
- **Mix.Sum+NIAH**: Combines summarization with needle-in-haystack search
|
372 |
+
- **Mix.RepoQA+KV**: Integrates code function retrieval with key-value lookup
|
373 |
+
- Requires multi-tasking or multi-step reasoning
|
374 |
+
|
375 |
+
## Two Shared Context Modes
|
376 |
+
The benchmark evaluates these tasks across two shared context modes:
|
377 |
+
- **Multi-turn Mode**: Caches context within single sessions
|
378 |
+
- **Multi-request Mode**: Shares context across multiple sessions
|
379 |
+
|
380 |
+
## Compared to previous long-context benchmarks
|
381 |
+
|
382 |
+
![SCBench](./data/comparison.png)
|
383 |
+
|
384 |
+
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.
|
385 |
+
|
386 |
+
## Results and Findings
|
387 |
+
|
388 |
+
![SCBench](./data/results.png)
|
389 |
+
|
390 |
+
Our SCBench reveals that the following key insights:
|
391 |
+
|
392 |
+
### Finding 1: Sub-O(n) Memory is Problematic in Multi-Request/Multi-Turn Decoding
|
393 |
+
- Sparse decoding methods with sub-O(n) memory perform well on first queries but lose accuracy in subsequent requests
|
394 |
+
- Methods maintaining O(n) memory with sub-O(n²) computation during pre-filling can better approximate full attention accuracy across multiple queries
|
395 |
+
|
396 |
+
### Finding 2: Task Performance Shows Varying Decline Patterns
|
397 |
+
- Sparse KV cache methods excel in tasks requiring global information processing
|
398 |
+
- O(n) memory is essential for tasks involving exact match retrieval
|
399 |
+
|
400 |
+
### Finding 3: Performance vs Compression Rate
|
401 |
+
- All methods show performance degradation as compression rates increase
|
402 |
+
- Sub-O(n) memory methods exhibit significant drop at 1/4 compression rate
|
403 |
+
- Methods like RetrievalAttention and KIVI that maintain O(n) memory with sparse decoding show better resilience at higher compression rates
|
404 |
+
|
405 |
+
### Finding 4: Issues with Long-Generation Scenarios
|
406 |
+
- Attention distribution shifts significantly as generation length and number of rounds increase
|
407 |
+
- This out-of-distribution (OOD) issue impacts performance even for O(n) memory methods
|
408 |
+
|
409 |
+
### Finding 5: Dynamic vs Static Patterns
|
410 |
+
- Dynamic sparse patterns generally outperform static patterns
|
411 |
+
|
412 |
+
## Citation
|
413 |
+
|
414 |
+
```bibtex
|
415 |
+
@article{li2024scbench,
|
416 |
+
title={SCBench: A KV cache-centric analysis of long-context methods},
|
417 |
+
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},
|
418 |
+
journal={arXiv preprint arXiv:2407.02490},
|
419 |
+
year={2024}
|
420 |
+
}
|
421 |
+
```
|