Abstract
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 API updates and version changes.
Gorilla 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 models and code are available at https://github.com/ShishirPatil/gorilla.
Example
Example API calls generated by GPT-4, Claude, and Gorilla for the
given prompt. In this example, GPT-4 presents a model that doesn’t exist, and Claude
picks an incorrect library. In contrast, our model, Gorilla, can identify the task correctly
and suggest a fully-qualified API call.
