Summary of Granite-function Calling Model: Introducing Function Calling Abilities Via Multi-task Learning Of Granular Tasks, by Ibrahim Abdelaziz et al.

Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks

by Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal, Sadhana Kumaravel, Matthew Stallone, Rameswar Panda, Yara Rizk, GP Bhargav, Maxwell Crouse, Chulaka Gunasekara, Shajith Ikbal, Sachin Joshi, Hima Karanam, Vineet Kumar, Asim Munawar, Sumit Neelam, Dinesh Raghu, Udit Sharma, Adriana Meza Soria, Dheeraj Sreedhar, Praveen Venkateswaran, Merve Unuvar, David Cox, Salim Roukos, Luis Lastras, Pavan Kapanipathi

First submitted to arxiv on: 27 Jun 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The paper introduces GRANITE-20B-FUNCTIONCALLING, an open large language model (LLM) capable of identifying, calling, and interacting with external tools and APIs to complete complex tasks. This ability, known as function calling, enables LLMs to access current information, outsource tasks, and perform multi-faceted functions like Nested Function Calling, Function Chaining, and Response Generation. The model is trained on seven fundamental tasks using a multi-task training approach and achieves the best performance among open models on the Berkeley Function Calling Leaderboard.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The paper develops an open large language model that can work with external tools and APIs to do complex tasks. This helps the model get information, do things it’s not good at, and be more useful. The new model is trained to do many different tasks and does well on a variety of challenges.