Large Language Models (LLMs) have seen an impressive wave of advances
recently, with models now excelling in a variety of tasks, such as mathematical
reasoning and program synthesis. However, their potential to effectively use
tools via API calls remains unfulfilled. This is a challenging task even for
today's state-of-the-art LLMs such as GPT-4, largely due to their inability to
generate accurate input arguments and their tendency to hallucinate the wrong
usage of an API call. We release Gorilla, a finetuned LLaMA-based model that
surpasses the performance of GPT-4 on writing API calls. When combined with a
document retriever, Gorilla demonstrates a strong capability to adapt to
test-time document changes, enabling flexible user updates or version changes.
It also substantially mitigates the issue of hallucination, commonly
encountered when prompting LLMs directly. To evaluate the model's ability, we
introduce APIBench, a comprehensive dataset consisting of HuggingFace,
TorchHub, and TensorHub APIs. The successful integration of the retrieval
system with Gorilla demonstrates the potential for LLMs to use tools more
accurately, keep up with frequently updated documentation, and consequently
increase the reliability and applicability of their outputs. Gorilla's code,
model, data, and demo are available at this https URL

本文介绍了 Gorilla 模型，该模型通过使用基于 LLaMA 的 finetuned 技术，在写 API 调用方面超越了最先进的 GPT-4 模型。结合文档检索器，Gorilla 展示了对测试时间文档更改的强大适应能力，减少了 LMM 直接提示时出现的幻觉问题，证明了 LLM 使用工具的潜力。