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20242026
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cs.CL2025

Continual Speech Learning with Fused Speech Features

Guitao Wang, Jinming Zhao, Hao Yang +3

Rapid growth in speech data demands adaptive models, as traditional static methods fail to keep pace with dynamic and diverse speech information. We introduce continuous speech lea…

cs.CL2025

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…

cs.CL2024

Audio Is the Achilles' Heel: Red Teaming Audio Large Multimodal Models

Hao Yang, Lizhen Qu, Ehsan Shareghi +1

Large Multimodal Models (LMMs) have demonstrated the ability to interact with humans under real-world conditions by combining Large Language Models (LLMs) and modality encoders to…

cs.CL2024

Double Mixture: Towards Continual Event Detection from Speech

Jingqi Kang, Tongtong Wu, Jinming Zhao +6

Speech event detection is crucial for multimedia retrieval, involving the tagging of both semantic and acoustic events. Traditional ASR systems often overlook the interplay between…

cs.CL2024

Jigsaw Puzzles: Splitting Harmful Questions to Jailbreak Large Language Models

Hao Yang, Lizhen Qu, Ehsan Shareghi +1

Large language models (LLMs) have exhibited outstanding performance in engaging with humans and addressing complex questions by leveraging their vast implicit knowledge and robust…

cs.CL2024

Towards Probing Speech-Specific Risks in Large Multimodal Models: A Taxonomy, Benchmark, and Insights

Hao Yang, Lizhen Qu, Ehsan Shareghi +1

Large Multimodal Models (LMMs) have achieved great success recently, demonstrating a strong capability to understand multimodal information and to interact with human users. Despit…