docs: update README

Author: ChenZiHong-Gavin

README.md

@@ -61,15 +61,15 @@ Furthermore, GraphGen incorporates multi-hop neighborhood sampling to capture co
 After data generation, you can use [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) and [xtuner](https://github.com/InternLM/xtuner) to finetune your LLMs.
 
 ## 📌 Latest Updates
-
+- **2026.01.15**: Added support for synthesizing single-choice, multiple-choice, and fill-in-the-blank question types, suitable for educational and evaluation scenarios.
 - **2025.12.26**: Added comprehensive knowledge graph evaluation metrics including accuracy assessment (entity/relation extraction quality), consistency assessment (conflict detection), and structural robustness assessment (noise ratio, connectivity, degree distribution).
 - **2025.12.16**: Added [rocksdb](https://github.com/facebook/rocksdb) for key-value storage backend and [kuzudb](https://github.com/kuzudb/kuzu) for graph database backend support.
-- **2025.12.16**: Added [vllm](https://github.com/vllm-project/vllm) for local inference backend support.
-- **2025.12.16**: Refactored the data generation pipeline using [ray](https://github.com/ray-project/ray) to improve the efficiency of distributed execution and resource management.
 
 <details>
 <summary>History</summary>
 
+- **2025.12.16**: Added [vllm](https://github.com/vllm-project/vllm) for local inference backend support.
+- **2025.12.16**: Refactored the data generation pipeline using [ray](https://github.com/ray-project/ray) to improve the efficiency of distributed execution and resource management.
 - **2025.12.1**: Added search support for [NCBI](https://www.ncbi.nlm.nih.gov/) and [RNAcentral](https://rnacentral.org/) databases, enabling extraction of DNA and RNA data from these bioinformatics databases.
 - **2025.10.30**: We support several new LLM clients and inference backends including [Ollama_client](https://github.com/open-sciencelab/GraphGen/blob/main/graphgen/models/llm/api/ollama_client.py), [http_client](https://github.com/open-sciencelab/GraphGen/blob/main/graphgen/models/llm/api/http_client.py), [HuggingFace Transformers](https://github.com/open-sciencelab/GraphGen/blob/main/graphgen/models/llm/local/hf_wrapper.py) and [SGLang](https://github.com/open-sciencelab/GraphGen/blob/main/graphgen/models/llm/local/sglang_wrapper.py).
 - **2025.10.23**: We support VQA(Visual Question Answering) data generation now. Run script: `bash scripts/generate/generate_vqa.sh`.
@@ -286,14 +286,17 @@ For any questions, please check [FAQ](https://github.com/open-sciencelab/GraphGe
 
    Pick the desired format and run the matching script:
 
-   | Format       | Script to run                                                          | Notes                                                                      |
-   | ------------ | ---------------------------------------------------------------------- | -------------------------------------------------------------------------- |
-   | `cot`        | `bash examples/generate/generate_cot_qa/generate_cot.sh`               | Chain-of-Thought Q\&A pairs                                                |
-   | `atomic`     | `bash examples/generate/generate_atomic_qa/generate_atomic.sh`         | Atomic Q\&A pairs covering basic knowledge                                 |
-   | `aggregated` | `bash examples/generate/generate_aggregated_qa/generate_aggregated.sh` | Aggregated Q\&A pairs incorporating complex, integrated knowledge          |
-   | `multi-hop`  | `examples/generate/generate_multi_hop_qa/generate_multi_hop.sh`        | Multi-hop reasoning Q\&A pairs                                             |
-   | `vqa`        | `bash examples/generate/generate_vqa/generate_vqa.sh`                  | Visual Question Answering pairs combining visual and textual understanding |
-
+   | Format          | Script to run                                                                | Notes                                                                      |
+   |-----------------|------------------------------------------------------------------------------|----------------------------------------------------------------------------|
+   | `cot`           | `bash examples/generate/generate_cot_qa/generate_cot.sh`                     | Chain-of-Thought Q\&A pairs                                                |
+   | `atomic`        | `bash examples/generate/generate_atomic_qa/generate_atomic.sh`               | Atomic Q\&A pairs covering basic knowledge                                 |
+   | `aggregated`    | `bash examples/generate/generate_aggregated_qa/generate_aggregated.sh`       | Aggregated Q\&A pairs incorporating complex, integrated knowledge          |
+   | `multi-hop`     | `examples/generate/generate_multi_hop_qa/generate_multi_hop.sh`              | Multi-hop reasoning Q\&A pairs                                             |
+   | `vqa`           | `bash examples/generate/generate_vqa/generate_vqa.sh`                        | Visual Question Answering pairs combining visual and textual understanding |
+   | `multi_choice`  | `bash examples/generate/generate_multi_choice_qa/generate_multi_choice.sh`   | Multiple-choice question-answer pairs                                      |
+   | `multi_answer`  | `bash examples/generate/generate_multi_answer_qa/generate_multi_answer.sh`   | Multiple-answer question-answer pairs                                      |
+   | `fill_in_blank` | `bash examples/generate/generate_fill_in_blank_qa/generate_fill_in_blank.sh` | Fill-in-the-blank question-answer pairs                                    |
+   
 
 4. Get the generated data
    ```bash

README_zh.md

@@ -62,14 +62,16 @@ GraphGen 首先根据源文本构建细粒度的知识图谱，然后利用期
 在数据生成后，您可以使用[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) 和 [xtuner](https://github.com/InternLM/xtuner)对大语言模型进行微调。
 
 ## 📌 最新更新
+- **2026.01.15**： 支持合成单选题、多选题和填空题型数据，适用于教育和评测场景。
 - **2025.12.26**: 新增知识图谱评估指标，包括准确度评估（实体/关系抽取质量）、一致性评估（冲突检测）和结构鲁棒性评估（噪声比、连通性、度分布）。
 - **2025.12.16**:新增 [rocksdb](https://github.com/facebook/rocksdb) 作为键值存储后端, [kuzudb](https://github.com/kuzudb/kuzu) 作为图数据库后端的支持。
-- **2025.12.16**:新增 [vllm](https://github.com/vllm-project/vllm) 作为本地推理后端的支持。
-- **2025.12.16**:使用 [ray](https://github.com/ray-project/ray) 重构了数据生成 pipeline，提升了分布式执行和资源管理的效率。
+
 
 <details>
 <summary>历史更新</summary>
 
+- **2025.12.16**:新增 [vllm](https://github.com/vllm-project/vllm) 作为本地推理后端的支持。
+- **2025.12.16**:使用 [ray](https://github.com/ray-project/ray) 重构了数据生成 pipeline，提升了分布式执行和资源管理的效率。
 - **2025.12.1**：新增对 [NCBI](https://www.ncbi.nlm.nih.gov/) 和 [RNAcentral](https://rnacentral.org/) 数据库的检索支持，现在可以从这些生物信息学数据库中提取DNA和RNA数据。
 - **2025.10.30**：我们支持多种新的 LLM 客户端和推理后端，包括 [Ollama_client]([Ollama_client](https://github.com/open-sciencelab/GraphGen/blob/main/graphgen/models/llm/api/ollama_client.py), [http_client](https://github.com/open-sciencelab/GraphGen/blob/main/graphgen/models/llm/api/http_client.py), [HuggingFace Transformers](https://github.com/open-sciencelab/GraphGen/blob/main/graphgen/models/llm/local/hf_wrapper.py) 和 [SGLang](https://github.com/open-sciencelab/GraphGen/blob/main/graphgen/models/llm/local/sglang_wrapper.py)。
 - **2025.10.23**：我们现在支持视觉问答（VQA）数据生成。运行脚本：`bash scripts/generate/generate_vqa.sh`。
@@ -283,13 +285,16 @@ GraphGen 首先根据源文本构建细粒度的知识图谱，然后利用期
 
    选择所需格式并运行对应脚本：
 
-   | 格式           | 运行脚本                                                                   | 说明              |
-   | ------------ | ---------------------------------------------------------------------- | --------------- |
-   | `cot`        | `bash examples/generate/generate_cot_qa/generate_cot.sh`               | 思维链问答对          |
-   | `atomic`     | `bash examples/generate/generate_atomic_qa/generate_atomic.sh`         | 覆盖基础知识的原子问答对    |
-   | `aggregated` | `bash examples/generate/generate_aggregated_qa/generate_aggregated.sh` | 整合复杂知识的聚合问答对    |
-   | `multi-hop`  | `bash examples/generate/generate_multi_hop_qa/generate_multi_hop.sh`   | 多跳推理问答对         |
-   | `vqa`        | `bash examples/generate/generate_vqa/generate_vqa.sh`                  | 视觉问答对，结合视觉和文本理解 |
+   | 格式              | 运行脚本                                                                         | 说明              |
+   |-----------------|------------------------------------------------------------------------------|-----------------|
+   | `cot`           | `bash examples/generate/generate_cot_qa/generate_cot.sh`                     | 思维链问答对          |
+   | `atomic`        | `bash examples/generate/generate_atomic_qa/generate_atomic.sh`               | 覆盖基础知识的原子问答对    |
+   | `aggregated`    | `bash examples/generate/generate_aggregated_qa/generate_aggregated.sh`       | 整合复杂知识的聚合问答对    |
+   | `multi_hop`     | `bash examples/generate/generate_multi_hop_qa/generate_multi_hop.sh`         | 多跳推理问答对         |
+   | `vqa`           | `bash examples/generate/generate_vqa/generate_vqa.sh`                        | 视觉问答对，结合视觉和文本理解 |
+   | `multi_choice`  | `bash examples/generate/generate_multi_choice_qa/generate_multi_choice.sh`   | 单项选择题问答对        |
+   | `multi_answer`  | `bash examples/generate/generate_multi_answer_qa/generate_multi_answer.sh`   | 多项选择题问答对        |
+   | `fill_in_blank` | `bash examples/generate/generate_fill_in_blank_qa/generate_fill_in_blank.sh` | 填空题问答对          |