11 citations · 43 across the 12 of their papers we have counts for
12 papers
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…
TESS: A Multi-intent Parser for Conversational Multi-Agent Systems with Decentralized Natural Language Understanding Models
Burak Aksar, Yara Rizk, Tathagata Chakraborti
Chatbots have become one of the main pathways for the delivery of business automation tools. Multi-agent systems offer a framework for designing chatbots at scale, making it easier…
TaskDiff: A Similarity Metric for Task-Oriented Conversations
Ankita Bhaumik, Praveen Venkateswaran, Yara Rizk +1
The popularity of conversational digital assistants has resulted in the availability of large amounts of conversational data which can be utilized for improved user experience and…
A Case for Business Process-Specific Foundation Models
Yara Rizk, Praveen Venkateswaran, Vatche Isahagian +1
The inception of large language models has helped advance state-of-the-art performance on numerous natural language tasks. This has also opened the door for the development of foun…
Natural Language Sentence Generation from API Specifications
Siyu Huo, Kushal Mukherjee, Jayachandu Bandlamudi +3
APIs are everywhere; they provide access to automation solutions that could help businesses automate some of their tasks. Unfortunately, they may not be accessible to the business…
Extending LIME for Business Process Automation
Sohini Upadhyay, Vatche Isahagian, Vinod Muthusamy +1
AI business process applications automate high-stakes business decisions where there is an increasing demand to justify or explain the rationale behind algorithmic decisions. Busin…