Gorilla: Large Language Model Connected with Massive APIs
This talk will introduce the Gorilla project, designed to connect LLMs with various services and applications exposed through APIs.
The Gorilla project is designed to connect large language models (LLMs) with a wide range of services and applications exposed through APIs. Imagine if ChatGPT could interact with thousands of services, ranging from Instagram and Doordash to tools like Google Calendar and Stripe, to help you accomplish tasks. This may be how we interact with computers and even the web in the future. Gorilla is an LLM that we train using a concept we call retriever-aware training (RAT), which picks the right API to perform a task that a user can specify in natural language. Gorilla also introduces an Abstract Syntax Tree (AST) based sub-tree matching algorithm, which for the first time allows us to measure hallucination of LLMs!
Speaker:
Shishir is a Ph.D. student in Computer Science at UC Berkeley advised by Joseph Gonzalez and Prabal Dutta affiliated with the Sky Computing Lab (previously RISE), Lab11, and Berkeley AI Research (BAIR). He is broadly interested in ML-Systems, and LLMs. Previously he has interned at Google Brain, and Amazon Science, and was at Microsoft Research as a Research Fellow before.
