11 citations · 54 across the 46 of their papers we have counts for
49 papers
Resurfacing Paralinguistic Awareness in Large Audio Language Models
Hao Yang, Minghan Wang, Tongtong Wu +3
Large Audio Language Models (LALMs) have expanded the interaction with human to speech modality, which introduces great interactive potential, due to the paralinguistic cues implic…
Privacy-R1: Privacy-Aware Multi-LLM Agent Collaboration via Reinforcement Learning
Zheng Hui, Yijiang River Dong, Sanhanat Sivapiromrat +2
When users submit queries to Large Language Models (LLMs), their prompts can often contain sensitive data, forcing a difficult choice: Send the query to a powerful proprietary LLM…
All Roads Lead to Rome: Graph-Based Confidence Estimation for Large Language Model Reasoning
Caiqi Zhang, Chang Shu, Ehsan Shareghi +1
Confidence estimation is essential for the reliable deployment of large language models (LLMs). Existing methods are primarily designed for factual QA tasks and often fail to gener…
Discrete Minds in a Continuous World: Do Language Models Know Time Passes?
Minghan Wang, Ye Bai, Thuy-Trang Vu +2
While Large Language Models (LLMs) excel at temporal reasoning tasks like event ordering and duration estimation, their ability to perceive the actual passage of time remains unexp…
Reshaping Representation Space to Balance the Safety and Over-rejection in Large Audio Language Models
Hao Yang, Lizhen Qu, Ehsan Shareghi +1
Large Audio Language Models (LALMs) have extended the capabilities of Large Language Models (LLMs) by enabling audio-based human interactions. However, recent research has revealed…
SpeechDialogueFactory: Generating High-Quality Speech Dialogue Data to Accelerate Your Speech-LLM Development
Minghan Wang, Ye Bai, Yuxia Wang +3
High-quality speech dialogue datasets are crucial for Speech-LLM development, yet existing acquisition methods face significant limitations. Human recordings incur high costs and p…