23 citations · 46 across the 18 of their papers we have counts for
4 papers · 1 filter
NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls
Kinjal Basu, Ibrahim Abdelaziz, Kiran Kate +10
The resurgence of autonomous agents built using large language models (LLMs) to solve complex real-world tasks has brought increased focus on LLMs' fundamental ability of tool or f…
Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks
Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal +23
Large language models (LLMs) have recently shown tremendous promise in serving as the backbone to agentic systems, as demonstrated by their performance in multi-faceted, challengin…
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
Mayank Mishra, Matt Stallone, Gaoyuan Zhang +43
Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environmen…
API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs
Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury +7
There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks. As such, there…