GraphGen

GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation

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📚 Table of Contents

• 📝 What is GraphGen?
• 📌 Latest Updates
• ⚙️ Support List
• 🚀 Quick Start
• 🏗️ System Architecture
• 🍀 Acknowledgements
• 📚 Citation
• 📜 License
• 📅 Star History

📝 What is GraphGen?

GraphGen is a framework for synthetic data generation guided by knowledge graphs. Please check the paper and best practice.

It begins by constructing a fine-grained knowledge graph from the source text，then identifies knowledge gaps in LLMs using the expected calibration error metric, prioritizing the generation of QA pairs that target high-value, long-tail knowledge. Furthermore, GraphGen incorporates multi-hop neighborhood sampling to capture complex relational information and employs style-controlled generation to diversify the resulting QA data.

After data generation, you can use LLaMA-Factory and xtuner to finetune your LLMs.

📌 Latest Updates

• 🎉 2026.04.13: The paper based on GraphGen, Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains, has been accepted to the ACL 2026 Main Conference! Congratulations! [arXiv][Code]
• 2026.02.04: We support HuggingFace Datasets as input data source for data generation now.
• 2026.01.15: LLM benchmark synthesis now supports single/multiple-choice & fill-in-the-blank & true-or-false—ideal for education 🌟🌟

History

• 2025.12.26: Knowledge graph evaluation metrics about accuracy (entity/relation), consistency (conflict detection), structural robustness (noise, connectivity, degree distribution)
• 2025.12.16: Added rocksdb for key-value storage backend and kuzudb for graph database backend support.
• 2025.12.16: Added vllm for local inference backend support.
• 2025.12.16: Refactored the data generation pipeline using ray to improve the efficiency of distributed execution and resource management.
• 2025.12.1: Added search support for NCBI and RNAcentral 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, http_client, HuggingFace Transformers and SGLang.
• 2025.10.23: We support VQA(Visual Question Answering) data generation now. Run script: bash scripts/generate/generate_vqa.sh.
• 2025.10.21: We support PDF as input format for data generation now via MinerU.
• 2025.09.29: We auto-update gradio demo on Hugging Face and ModelScope.
• 2025.08.14: We have added support for community detection in knowledge graphs using the Leiden algorithm, enabling the synthesis of Chain-of-Thought (CoT) data.
• 2025.07.31: We have added Google, Bing, Wikipedia, and UniProt as search back-ends.
• 2025.04.21: We have released the initial version of GraphGen.

Effectiveness of GraphGen

Pretrain

Inspired by Kimi-K2’s technical report (Improving Token Utility with Rephrasing) and ByteDance Seed’s Reformulation for Pretraining Data Augmentation (MGA framework), GraphGen added a rephrase pipeline — using LLM-driven reformulation to generate diverse variants of the same corpus instead of redundant repetition.

Setup: Qwen3-0.6B trained from scratch on SlimPajama-6B.

Method	ARC-E	ARC-C	HellaSwag	GSM8K	TruthfulQA-MC1	TruthfulQA-MC2	Average
SlimPajama-6B trained for 2 epochs	25.55	21.08	24.48	0.08	24.36	49.90	24.24
SlimPajama-6B + Executive-Summary Rephrase trained for 1 epoch	26.43	22.70	24.75	1.36	26.19	51.90	25.56(↑1.32)
SlimPajama-6B + Cross-Domain Rephrase trained for 1 epoch	28.79	20.22	24.46	0.00	24.97	52.41	25.14(↑0.9)

Both rephrase methods lift the average by ~1 point over the baseline with zero additional data — all gains come from how the same knowledge is expressed.

SFT

Here is post-training result which over 50% SFT data comes from GraphGen and our data clean pipeline.

Domain	Dataset	Ours	Qwen2.5-7B-Instruct (baseline)
Plant	SeedBench	65.9	51.5
Common	CMMLU	73.6	75.8
Knowledge	GPQA-Diamond	40.0	33.3
Math	AIME24	20.6	16.7
	AIME25	22.7	7.2

RLVR

Reinforcement Learning with Verifiable Rewards (RLVR) has demonstrated promising potential to enhance the reasoning capabilities of large language models (LLMs). However, its applications on knowledge-intensive domains have not been effectively explored due to the scarcity of high-quality verifiable data. By leveraging GraphGen for automated verifiable data synthesis, we extend RLVR to these broader domains. We applied reinforcement learning directly to the Qwen2.5-7B base model using the synthesized data without any prior SFT (Code). Here are the results.

Domain	Dataset	Ours	Qwen2.5-7B-Instruct (baseline)
Plant	SeedBench	66.8	51.5
Law	LawBench	55.2	54.76
Medicine	MedQA	87.1	80.7
General	BBH	55.3	49.6

More details can be found at examples/generate/generate_masked_fill_in_blank_qa.

⚙️ Support List

We support various LLM inference servers, API servers, inference clients, input file formats, data modalities, output data formats, and output data types. Users can flexibly configure according to the needs of synthetic data.

Inference Server	Api Server	Inference Client	Data Source	Data Modal	Data Type
HF SGLang vllm	Silicon OpenAI Azure	HTTP Ollama OpenAI	Files(CSV, JSON, PDF, TXT, etc.) Databases(UniProt, NCBI, RNAcentral) Search Engines(Bing, Google) Knowledge Graphs(Wikipedia)	TEXT IMAGE	Aggregated Atomic CoT Multi-hop VQA

🚀 Quick Start

Experience GraphGen Demo through Huggingface or Modelscope.

For any questions, please check FAQ, open new issue or join our wechat group and ask.

Preparation

1. Install uv

   # You could try pipx or pip to install uv when meet network issues, refer the uv doc for more details
   curl -LsSf https://astral.sh/uv/install.sh | sh
   

2. Clone the repository

   git clone --depth=1 https://github.com/open-sciencelab/GraphGen
   cd GraphGen
   

3. Create a new uv environment

    uv venv --python 3.10
   

4. Configure the dependencies

   uv pip install -r requirements.txt
   

Run Gradio Demo

python -m webui.app

Run from PyPI

1. Install GraphGen

   uv pip install graphg
   

2. Run in CLI

   SYNTHESIZER_MODEL=your_synthesizer_model_name \
   SYNTHESIZER_BASE_URL=your_base_url_for_synthesizer_model \
   SYNTHESIZER_API_KEY=your_api_key_for_synthesizer_model \
   TRAINEE_MODEL=your_trainee_model_name \
   TRAINEE_BASE_URL=your_base_url_for_trainee_model \
   TRAINEE_API_KEY=your_api_key_for_trainee_model \
   graphg --output_dir cache
   

Run from Source

1. Configure the environment

   • Create an .env file in the root directory

     cp .env.example .env
     

   • Set the following environment variables:

      # Tokenizer
      TOKENIZER_MODEL=
      
      # LLM
      # Support different backends: http_api, openai_api, ollama_api, ollama, huggingface, tgi, sglang, tensorrt
      # Synthesizer is the model used to construct KG and generate data
      # Trainee is the model used to train with the generated data
     
      # http_api / openai_api
      SYNTHESIZER_BACKEND=openai_api
      SYNTHESIZER_MODEL=gpt-4o-mini
      SYNTHESIZER_BASE_URL=
      SYNTHESIZER_API_KEY=
      TRAINEE_BACKEND=openai_api
      TRAINEE_MODEL=gpt-4o-mini
      TRAINEE_BASE_URL=
      TRAINEE_API_KEY=
      
      # azure_openai_api
      # SYNTHESIZER_BACKEND=azure_openai_api
      # The following is the same as your "Deployment name" in Azure
      # SYNTHESIZER_MODEL=<your-deployment-name>
      # SYNTHESIZER_BASE_URL=https://<your-resource-name>.openai.azure.com/openai/deployments/<your-deployment-name>/chat/completions
      # SYNTHESIZER_API_KEY=
      # SYNTHESIZER_API_VERSION=<api-version>
      
      # # ollama_api
      # SYNTHESIZER_BACKEND=ollama_api
      # SYNTHESIZER_MODEL=gemma3
      # SYNTHESIZER_BASE_URL=http://localhost:11434
      #
      # Note: TRAINEE with ollama_api backend is not supported yet as ollama_api does not support logprobs.
      
      # # huggingface
      # SYNTHESIZER_BACKEND=huggingface
      # SYNTHESIZER_MODEL=Qwen/Qwen2.5-0.5B-Instruct
      #
      # TRAINEE_BACKEND=huggingface
      # TRAINEE_MODEL=Qwen/Qwen2.5-0.5B-Instruct
      
      # # sglang
      # SYNTHESIZER_BACKEND=sglang
      # SYNTHESIZER_MODEL=Qwen/Qwen2.5-0.5B-Instruct
      # SYNTHESIZER_TP_SIZE=1
      # SYNTHESIZER_NUM_GPUS=1
      
      # TRAINEE_BACKEND=sglang
      # TRAINEE_MODEL=Qwen/Qwen2.5-0.5B-Instruct
      # SYNTHESIZER_TP_SIZE=1
      # SYNTHESIZER_NUM_GPUS=1
      
      # # vllm
      # SYNTHESIZER_BACKEND=vllm
      # SYNTHESIZER_MODEL=Qwen/Qwen2.5-0.5B-Instruct
      # SYNTHESIZER_NUM_GPUS=1
      
      # TRAINEE_BACKEND=vllm
      # TRAINEE_MODEL=Qwen/Qwen2.5-0.5B-Instruct
      # TRAINEE_NUM_GPUS=1
     

2. (Optional) Customize generation parameters in config.yaml .

   Edit the corresponding YAML file, e.g.:

     # examples/generate/generate_aggregated_qa/aggregated_config.yaml
     global_params:
       working_dir: cache
       graph_backend: kuzu # graph database backend, support: kuzu, networkx
       kv_backend: rocksdb # key-value store backend, support: rocksdb, json_kv
   
     nodes:
       - id: read_files # id is unique in the pipeline, and can be referenced by other steps
         op_name: read
         type: source
         dependencies: []
         params:
           input_path:
             - examples/input_examples/jsonl_demo.jsonl # input file path, support json, jsonl, txt, pdf. See examples/input_examples for examples
   
     # additional settings...
   

3. Generate data

   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
   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
   true_false	bash examples/generate/generate_true_false_qa/generate_true_false.sh	True-or-false question-answer pairs

4. Get the generated data

   ls cache/output
   

Run with Docker

1. Build the Docker image

   docker build -t graphgen .
   

2. Run the Docker container

    docker run -p 7860:7860 graphgen
   

🏗️ System Architecture

See analysis by deepwiki for a technical overview of the GraphGen system, its architecture, and core functionalities.

Workflow

🍀 Acknowledgements

• SiliconFlow Abundant LLM API, some models are free
• LightRAG Simple and efficient graph retrieval solution
• ROGRAG A robustly optimized GraphRAG framework
• DB-GPT An AI native data app development framework

📚 Citation

If you find this repository useful, please consider citing our work:

@misc{chen2025graphgenenhancingsupervisedfinetuning,
      title={GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation}, 
      author={Zihong Chen and Wanli Jiang and Jinzhe Li and Zhonghang Yuan and Huanjun Kong and Wanli Ouyang and Nanqing Dong},
      year={2025},
      eprint={2505.20416},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.20416}, 
}

📜 License

This project is licensed under the Apache License 2.0.

📅 Star History