Gorilla: LLMs Connected to APIs Explained

The article presents a summary video of the paper "Gorilla: Large Language Models connected to Massive APIs" by Patil et al. 2023. The paper discusses how Large Language Models (LLMs) can be enhanced by connecting them with external tools such as search engines, code executors, calculators, calendars, emails, CRM, etc. While GPT-4 is proficient at formatting API requests without additional training, Gorilla demonstrates that specialized training can significantly improve performance.

The article also highlights that this improved performance can be achieved with a more cost-effective 7 billion parameter model, derived by fine-tuning the Meta AI LlaMA-2 7B checkpoint. The video covers various aspects of the paper, including the APIBench dataset, Self-Instruct training data generation, Retrieval-Aware Training, and other details about Gorilla. The author encourages viewers to stay tuned for Weaviate Gorilla and is open to answering questions or discussing ideas related to the video's content.

Key takeaways:

The paper presents Gorilla, a large language model (LLM) that is connected to massive APIs and has been trained specifically for this purpose, outperforming GPT-4 in formatting API requests.
Gorilla achieves high accuracy performance with a cheaper 7 billion parameter model, derived by fine-tuning the Meta AI LlaMA-2 7B checkpoint.
The paper covers various interesting details including the APIBench dataset, Self-Instruct training data generation, and Retrieval-Aware Training.
A new version, Weaviate Gorilla, is expected to be released soon.

Gorilla: LLMs Connected to APIs Explained

Key takeaways:

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