高质量合成数据能显著提升工具使用大模型性能，即使数据量更少。
Quality Matters: Evaluating Synthetic Data for Tool-Using LLMs
- 设计双方法评估工具调用数据可靠性：人工规则与模型自评。
- 高质数据训练的模型表现优于未经验证的数据，即使数据量更小。
- 适合关注数据质量、提升模型实用性的研究人员和工程师。
训练大型语言模型（LLMs）以使用外部工具是一个快速发展的领域，近期研究聚焦于生成合成数据来缓解数据不足的问题。然而，缺乏系统性的数据质量检查给模型的训练和测试带来了挑战。为此，我们提出了两种评估工具使用数据可靠性的方法：第一种采用直观的人工定义正确性标准；第二种则通过上下文内评估的模型驱动方法进行评估。我们在两个主流基准上进行了全面的数据质量评估，并进一步开展外在评估，展示数据质量对模型性能的影响。结果表明，使用高质量数据训练的模型在性能上优于使用未经验证数据训练的模型，即便训练数据量更小。这些发现从实证上支持了评估和确保工具使用型大模型训练数据可靠性的必要性。
原文摘要 · Abstract (English)
Training large language models (LLMs) for external tool usage is a rapidly expanding field, with recent research focusing on generating synthetic data to address the shortage of available data. However, the absence of systematic data quality checks poses complications for properly training and testing models. To that end, we propose two approaches for assessing the reliability of data for training LLMs to use external tools. The first approach uses intuitive, human-defined correctness criteria. The second approach uses a model-driven assessment with in-context evaluation. We conduct a thorough evaluation of data quality on two popular benchmarks, followed by an extrinsic evaluation that showcases the impact of data quality on model performance. Our results demonstrate that models trained on high-quality data outperform those trained on unvalidated data, even when trained with a smaller quantity of data. These findings empirically support the significance of assessing and ensuring the reliability of training data for tool-using LLMs.
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