1 citations · 2 across the 4 of their papers we have counts for
4 papers
LMK > CLS: Landmark Pooling for Dense Embeddings
Meet Doshi, Aashka Trivedi, Vishwajeet Kumar +5
Representation learning is central to many downstream tasks such as search, clustering, classification, and reranking. State-of-the-art sequence encoders typically collapse a varia…
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…
An Approach to Build Zero-Shot Slot-Filling System for Industry-Grade Conversational Assistants
G P Shrivatsa Bhargav, Sumit Neelam, Udit Sharma +10
We present an approach to build Large Language Model (LLM) based slot-filling system to perform Dialogue State Tracking in conversational assistants serving across a wide variety o…
Evaluating Chatbots to Promote Users' Trust -- Practices and Open Problems
Biplav Srivastava, Kausik Lakkaraju, Tarmo Koppel +3
Chatbots, the common moniker for collaborative assistants, are Artificial Intelligence (AI) software that enables people to naturally interact with them to get tasks done. Although…