1 citations · 1 across the 3 of their papers we have counts for
4 papers
TOD-ProcBench: Benchmarking Complex Instruction-Following in Task-Oriented Dialogues
Sarik Ghazarian, Abhinav Gullapalli, Swair Shah +4
In real-world task-oriented dialogue (TOD) settings, agents are required to strictly adhere to complex instructions while conducting multi-turn conversations with customers. These…
Semantic Volume: Quantifying and Detecting both External and Internal Uncertainty in LLMs
Xiaomin Li, Zhou Yu, Ziji Zhang +4
Large language models (LLMs) have demonstrated remarkable performance across diverse tasks by encoding vast amounts of factual knowledge. However, they are still prone to hallucina…
Analysis of Indic Language Capabilities in LLMs
Aatman Vaidya, Tarunima Prabhakar, Denny George +1
This report evaluates the performance of text-in text-out Large Language Models (LLMs) to understand and generate Indic languages. This evaluation is used to identify and prioritiz…
DARD: A Multi-Agent Approach for Task-Oriented Dialog Systems
Aman Gupta, Anirudh Ravichandran, Ziji Zhang +3
Task-oriented dialogue systems are essential for applications ranging from customer service to personal assistants and are widely used across various industries. However, developin…