Visually-grounded dialog systems, which integrate multiple modes of
comm...
Finetuning large language models (LLMs) on instructions leads to vast
pe...
Training large language models (LLMs) with open-domain instruction data ...
Large language models (LLMs) demonstrate remarkable ability to comprehen...
Existing multimodal task-oriented dialog data fails to demonstrate the
d...
Perceiving multi-modal information and fulfilling dialogues with humans ...
Large language models (LLMs) have exhibited an emergent in-context learn...
Out-of-Domain (OOD) intent detection is vital for practical dialogue sys...
In a transfer-based attack against Automatic Speech Recognition (ASR)
sy...
Table-based reasoning has shown remarkable progress in combining deep mo...
Existing multimodal conversation agents have shown impressive abilities ...
In this paper, we propose a novel SQL guided pre-training framework STAR...
Pre-training methods with contrastive learning objectives have shown
rem...
This paper aims to improve the performance of text-to-SQL parsing by
exp...
Multi-view learning has progressed rapidly in recent years. Although man...
Text-to-SQL parsing is an essential and challenging task. The goal of
te...
The importance of building text-to-SQL parsers which can be applied to n...
The task of converting a natural language question into an executable SQ...
Recently pre-training models have significantly improved the performance...
Text-to-SQL aims to map natural language questions to SQL queries. The
s...
Semantic parsing has long been a fundamental problem in natural language...
Realistic speech-driven 3D facial animation is a challenging problem due...
Multi-view subspace clustering aims to discover the inherent structure b...