17 citations · 22 across the 5 of their papers we have counts for
7 papers
Transforming Expert Knowledge into Scalable Ontology via Large Language Models
Ikkei Itoku, David Theil, Evelyn Eichelsdoerfer Uehara +7
Having a unified, coherent taxonomy is essential for effective knowledge representation in domain-specific applications as diverse terminologies need to be mapped to underlying con…
THELMA: Task Based Holistic Evaluation of Large Language Model Applications-RAG Question Answering
Udita Patel, Rutu Mulkar, Jay Roberts +6
We propose THELMA (Task Based Holistic Evaluation of Large Language Model Applications), a reference free framework for RAG (Retrieval Augmented generation) based question answerin…
Potential and Perils of Large Language Models as Judges of Unstructured Textual Data
Rewina Bedemariam, Natalie Perez, Sreyoshi Bhaduri +7
Rapid advancements in large language models have unlocked remarkable capabilities when it comes to processing and summarizing unstructured text data. This has implications for the…
HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent
Weijie Xu, Zicheng Huang, Wenxiang Hu +5
Recent advancements in Large Language Models (LLMs) have been reshaping Natural Language Processing (NLP) task in several domains. Their use in the field of Human Resources (HR) ha…
On Regret-Optimal Learning in Decentralized Multi-player Multi-armed Bandits
Naumaan Nayyar, Dileep Kalathil, Rahul Jain
We consider the problem of learning in single-player and multiplayer multiarmed bandit models. Bandit problems are classes of online learning problems that capture exploration vers…
Optimal Decentralized Control with Asymmetric One-Step Delayed Information Sharing
Naumaan Nayyar, Dileep Kalathil, Rahul Jain
We consider optimal control of decentralized LQG problems for plants controlled by two players having asymmetric information sharing patterns between them. In one scenario, players…
Decentralized Learning for Multi-player Multi-armed Bandits
Dileep Kalathil, Naumaan Nayyar, Rahul Jain
We consider the problem of distributed online learning with multiple players in multi-armed bandits (MAB) models. Each player can pick among multiple arms. When a player picks an a…