17 citations · 36 across the 8 of their papers we have counts for
9 papers
XingChen-UniFE: A Unified Generative Speech Front-End via Complementary Acoustic-Semantic Modeling
Kelan Kuang, Qihan Hu, Jian Kang +3
This paper presents XingChen-UniFE, a unified large audio language model (LALM) for general speech restoration (GSR) and target speaker extraction (TSE). The proposed model employs…
SIGHT: Reinforcement Learning with Self-Evidence and Information-Gain Diverse Branching for Search Agent
Wenlin Zhong, Jinluan Yang, Yiquan Wu +3
Reinforcement Learning (RL) has empowered Large Language Models (LLMs) to master autonomous search for complex question answering. However, particularly within multi-turn search sc…
STEAMROLLER: A Multi-Agent System for Inclusive Automatic Speech Recognition for People who Stutter
Ziqi Xu, Yi Liu, Yuekang Li +3
People who stutter (PWS) face systemic exclusion in today's voice-driven society, where access to voice assistants, authentication systems, and remote work tools increasingly depen…
Breaking the Loop: Detecting and Mitigating Denial-of-Service Vulnerabilities in Large Language Models
Junzhe Yu, Yi Liu, Huijia Sun +2
Large Language Models (LLMs) have significantly advanced text understanding and generation, becoming integral to applications across education, software development, healthcare, en…
Detecting LLM Fact-conflicting Hallucinations Enhanced by Temporal-logic-based Reasoning
Ningke Li, Yahui Song, Kailong Wang +4
Large language models (LLMs) face the challenge of hallucinations -- outputs that seem coherent but are actually incorrect. A particularly damaging type is fact-conflicting halluci…
DistillSeq: A Framework for Safety Alignment Testing in Large Language Models using Knowledge Distillation
Mingke Yang, Yuqi Chen, Yi Liu +1
Large Language Models (LLMs) have showcased their remarkable capabilities in diverse domains, encompassing natural language understanding, translation, and even code generation. Th…
Self and Cross-Model Distillation for LLMs: Effective Methods for Refusal Pattern Alignment
Jie Li, Yi Liu, Chongyang Liu +4
Large Language Models (LLMs) like OpenAI's GPT series, Anthropic's Claude, and Meta's LLaMa have shown remarkable capabilities in text generation. However, their susceptibility to…
A Cross-Language Investigation into Jailbreak Attacks in Large Language Models
Jie Li, Yi Liu, Chongyang Liu +5
Large Language Models (LLMs) have become increasingly popular for their advanced text generation capabilities across various domains. However, like any software, they face security…
Generative AI to Generate Test Data Generators
Benoit Baudry, Khashayar Etemadi, Sen Fang +7
Generating fake data is an essential dimension of modern software testing, as demonstrated by the number and significance of data faking libraries. Yet, developers of faking librar…