Gorilla: Large Language Model Connected with Massive APIs
This addresses the challenge of improving tool use via APIs for LLMs, which is crucial for enhancing their reliability and applicability in real-world tasks, though it is an incremental advancement building on existing models.
The paper tackles the problem of large language models (LLMs) struggling to generate accurate API calls due to hallucination and incorrect input arguments, and it introduces Gorilla, a finetuned LLaMA-based model that surpasses GPT-4 in writing API calls and substantially mitigates hallucination issues.
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 https://gorilla.cs.berkeley.edu