8 citations · 17 across the 8 of their papers we have counts for
4 papers · 1 filter
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking
Atharva Kulkarni, Bo-Hsiang Tseng, Joel Ruben Antony Moniz +3
In-context learning with Large Language Models (LLMs) has emerged as a promising avenue of research in Dialog State Tracking (DST). However, the best-performing in-context learning…
Can Large Language Models Understand Context?
Yilun Zhu, Joel Ruben Antony Moniz, Shruti Bhargava +6
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent. Howe…
MARRS: Multimodal Reference Resolution System
Halim Cagri Ates, Shruti Bhargava, Site Li +15
Successfully handling context is essential for any dialog understanding task. This context maybe be conversational (relying on previous user queries or system responses), visual (r…
Intelligent Assistant Language Understanding On Device
Cecilia Aas, Hisham Abdelsalam, Irina Belousova +20
It has recently become feasible to run personal digital assistants on phones and other personal devices. In this paper we describe a design for a natural language understanding sys…