Improve dataset card: Add paper link, update name, expand configs, and enhance description

README.md

@@ -1,22 +1,65 @@
 ---
+language:
+- en
 task_categories:
 - question-answering
 - summarization
 - text-generation
-language:
-- en
+pretty_name: LoopServe Multi-Turn Dialogue Benchmark
 tags:
 - llm
 - kv_cache
-pretty_name: MKV_Cache
-
 configs:
-- config_name: question-answering
-  data_files: question-answering/*.jsonl
-- config_name: summarization
-  data_files: summarization/*.jsonl
+- config_name: conversations
+  data_files: conversations.jsonl
+- config_name: multi_turn_few_shot_learning
+  data_files: multi_turn/few_shot_learning/*.jsonl
+- config_name: multi_turn_needle_in_haystack
+  data_files: multi_turn/needle_in_haystack/*.jsonl
+- config_name: multi_turn_question_answering
+  data_files: multi_turn/question_answering/*.jsonl
+- config_name: multi_turn_summarization
+  data_files: multi_turn/summarization/*.jsonl
+- config_name: single_turn_few_shot_learning
+  data_files: single_turn/few_shot_learning/*.jsonl
+- config_name: single_turn_needle_in_haystack
+  data_files: single_turn/needle_in_haystack/*.jsonl
+- config_name: single_turn_question_answering
+  data_files: single_turn/question_answering/*.jsonl
+- config_name: single_turn_summarization
+  data_files: single_turn/summarization/*.jsonl
 ---
 
+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)**.
+
+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.
+
+**Paper:** [LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues](https://huggingface.co/papers/2507.13681)
+
+### Sample Usage
+
+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:
+
+```python
+from datasets import load_dataset
+
+# Load the multi-turn question-answering subset
+dataset_qa_multi = load_dataset("MKV_Cache", "multi_turn_question_answering")
+print(dataset_qa_multi)
+
+# Load the single-turn summarization subset
+dataset_sum_single = load_dataset("MKV_Cache", "single_turn_summarization")
+print(dataset_sum_single)
+
+# Load the base conversations data
+dataset_conv = load_dataset("MKV_Cache", "conversations")
+print(dataset_conv)
+```
+
+### Dataset Structure
+
+The repository contains the following file structure for the benchmark data:
+
 ``` shell
 .
 ├── README.md