There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks. As such, there is tremendous interest in methods that can acquire sufficient quantities of train and test data that involve calls to tools / APIs. Two lines of research have emerged as the predominant strategies for addressing this challenge. The first has focused on synthetic data generation techniques, while the second has involved curating task-adjacent datasets which can be transformed into API / Tool-based tasks. In this paper, we focus on the task of identifying, curating, and transforming existing datasets and, in turn, introduce API-BLEND, a large corpora for training and systematic testing of tool-augmented LLMs. The datasets mimic real-world scenarios involving API-tasks such as API / tool detection, slot filling, and sequencing of the detected APIs. We demonstrate the utility of the API-BLEND dataset for both training and benchmarking purposes.

翻译：随着大语言模型（LLM）在有效使用工具和外部应用程序编程接口（API）以规划完成任务方面的需求日益增长，获取足够数量涉及工具/API调用的训练与测试数据的方法备受关注。当前应对这一挑战的主流策略主要分为两类研究方向：第一类聚焦于合成数据生成技术，第二类则致力于整理可转换为API/工具任务的相邻数据集。本文专注于识别、整理和转换现有数据集的工作，并由此提出API-BLEND——一个用于系统化训练与评估工具增强型大语言模型的大型语料库。该数据集模拟了涉及API任务（如API/工具检测、槽位填充及检测到的API排序）的真实场景。我们证明了API-BLEND数据集在训练与基准测试两方面的实用价值。