In this talk, I will present our vision of AgenticSystems, and introduce our innovative approach in integrating Large Language Models (LLMs) with various tools via APIs. Connecting LLMs with APIs presents a significant challenge, as these models often struggle to generate precise input arguments and are prone to hallucinating API calls. To address this, we developed Gorilla LLM, trained with our novel Retriever-Aware-Training (RAT), setting a new benchmark for tool-use in LLMs. Gorilla also introduces a programming language-inspired metric to quantify hallucinations, a common issue in LLMs. I will conclude by presenting GoEx, a runtime to execute actions generated by LLMs —such as code and API calls—across agents, workflows, and LLM-powered microservices. A key innovation in GoEx is the incorporation of "undo" and "damage confinement" abstractions to mitigate unintended actions and risks.
The Gorilla project kick-started tool-calling in LLMs, and with millions of user requests, widespread enterprise adoption—including all leading LLM labs—and a thriving open-source community, the Gorilla project continues to shape the evolving field of tool-calling for agentic LLMs.
Bio: Shishir G. Patil is a PhD from UC Berkeley where he was advised by Joseph Gonzalez, Prabal Dutta, and Ion Stoica. He is interested in designing and building efficient machine-learning systems. Recently, his focus has been on teaching LLMs to use tools through API calls. His works include Gorilla LLM, RAFT, OpenFunctions, Berkeley Function Calling Leaderboard, Skyplane, and POET. He was a Research Fellow at Microsoft Research before starting his PhD.
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