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--- |
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language: |
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- en |
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task_categories: |
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- question-answering |
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- summarization |
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- text-generation |
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pretty_name: LoopServe Multi-Turn Dialogue Benchmark |
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tags: |
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- llm |
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- kv_cache |
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configs: |
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- config_name: conversations |
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data_files: conversations.jsonl |
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- config_name: multi_turn_few_shot_learning |
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data_files: multi_turn/few_shot_learning/*.jsonl |
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- config_name: multi_turn_needle_in_haystack |
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data_files: multi_turn/needle_in_haystack/*.jsonl |
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- config_name: multi_turn_question_answering |
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data_files: multi_turn/question_answering/*.jsonl |
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- config_name: multi_turn_summarization |
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data_files: multi_turn/summarization/*.jsonl |
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- config_name: single_turn_few_shot_learning |
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data_files: single_turn/few_shot_learning/*.jsonl |
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- config_name: single_turn_needle_in_haystack |
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data_files: single_turn/needle_in_haystack/*.jsonl |
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- config_name: single_turn_question_answering |
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data_files: single_turn/question_answering/*.jsonl |
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- config_name: single_turn_summarization |
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data_files: single_turn/summarization/*.jsonl |
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--- |
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This repository contains the benchmark datasets proposed in the paper **[LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues](https://huggingface.co/papers/2507.13681)**. |
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The LoopServe benchmark introduces eleven multi-turn datasets designed to evaluate large language models (LLMs) on realistic query positions and conversational dependencies. This is crucial for assessing LLM inference acceleration methods in dynamic, multi-turn dialogue settings common in applications like chatbots and virtual assistants. |
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**Paper:** [LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues](https://huggingface.co/papers/2507.13681) |
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### Sample Usage |
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You can load different subsets of the dataset using the `load_dataset` function from the `datasets` library. For example, to load the `multi_turn_question_answering` subset: |
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```python |
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from datasets import load_dataset |
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# Load the multi-turn question-answering subset |
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dataset_qa_multi = load_dataset("MKV_Cache", "multi_turn_question_answering") |
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print(dataset_qa_multi) |
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# Load the single-turn summarization subset |
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dataset_sum_single = load_dataset("MKV_Cache", "single_turn_summarization") |
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print(dataset_sum_single) |
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# Load the base conversations data |
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dataset_conv = load_dataset("MKV_Cache", "conversations") |
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print(dataset_conv) |
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``` |
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### Dataset Structure |
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The repository contains the following file structure for the benchmark data: |
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``` shell |
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. |
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├── README.md |
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├── conversations.jsonl |
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├── multi_turn |
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│ ├── few_shot_learning |
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│ ├── needle_in_haystack |
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│ ├── question_answering |
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│ └── summarization |
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└── single_turn |
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├── few_shot_learning |
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├── needle_in_haystack |
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├── question_answering |
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└── summarization |
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``` |
