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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.
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## 📌 Latest Updates
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- **2026.01.15**: Added support for synthesizing single-choice, multiple-choice, and fill-in-the-blank question types, suitable for educational and evaluation scenarios.
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- **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).
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- **2026.01.15**: **LLM benchmark synthesis** now supports single/multiple-choice & fill-in-the-blank—ideal for education 🌟🌟
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- **2025.12.26**: Knowledge graph evaluation metrics about accuracy (entity/relation), consistency (conflict detection), structural robustness (noise, connectivity, degree distribution)
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- **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.
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<details>

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