7 citations · 22 across the 9 of their papers we have counts for
9 papers
From Where Things Are to What They Are For: Benchmarking Spatial-Functional Intelligence in Multimodal LLMs
Le Zhang, Jihan Yang, Soundarya Krishnan +11
Human-level agentic intelligence extends beyond low-level geometric perception, evolving from recognizing where things are to understanding what they are for. While existing benchm…
Evaluating Evaluation Metrics -- The Mirage of Hallucination Detection
Atharva Kulkarni, Yuan Zhang, Joel Ruben Antony Moniz +5
Hallucinations pose a significant obstacle to the reliability and widespread adoption of language models, yet their accurate measurement remains a persistent challenge. While many…
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…
STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants
Leon Liyang Zhang, Jiarui Lu, Joel Ruben Antony Moniz +5
In the context of a voice assistant system, steering refers to the phenomenon in which a user issues a follow-up command attempting to direct or clarify a previous turn. We propose…
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…
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues
Bo-Hsiang Tseng, Shruti Bhargava, Jiarui Lu +4
Anaphora and ellipses are two common phenomena in dialogues. Without resolving referring expressions and information omission, dialogue systems may fail to generate consistent and…
Conversational Semantic Parsing for Dialog State Tracking
Jianpeng Cheng, Devang Agrawal, Hector Martinez Alonso +12
We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog. By formulating DST as a semantic parsing task ove…