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

• URL: http://arxiv.org/abs/2505.20416v1
• Date: Mon, 26 May 2025 18:06:50 GMT
• Title: GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation
• Authors: Zihong Chen, Wanli Jiang, Jinzhe Li, Zhonghang Yuan, Huanjun Kong, Wanli Ouyang, Nanqing Dong,
• Abstract summary: Fine-tuning for large language models (LLMs) typically requires substantial amounts of high-quality supervised data.<n>Existing approaches suffer from factual inaccuracies, insufficient long-tail coverage, simplistic knowledge structures, and homogenized outputs.<n>We introduce GraphGen, a knowledge graph-guided framework designed for three key question-answering (QA) scenarios.
• Score: 41.31575016578663
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Fine-tuning for large language models (LLMs) typically requires substantial amounts of high-quality supervised data, which is both costly and labor-intensive to acquire. While synthetic data generation has emerged as a promising solution, existing approaches frequently suffer from factual inaccuracies, insufficient long-tail coverage, simplistic knowledge structures, and homogenized outputs. To address these challenges, we introduce GraphGen, a knowledge graph-guided framework designed for three key question-answering (QA) scenarios: atomic QA, aggregated QA, and multi-hop QA. It begins by constructing a fine-grained knowledge graph from the source text. It 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. Experimental results on knowledge-intensive tasks under closed-book settings demonstrate that GraphGen outperforms conventional synthetic data methods, offering a more reliable and comprehensive solution to the data scarcity challenge in supervised fine-tuning. The code and data are publicly available at https://github.com/open-sciencelab/GraphGen.

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