173 citations · 644 across the 150 of their papers we have counts for
115 papers · 1 filter
UniGuard: Multi-View Guardrail Learning for Cross-Interface Safety Generalization
Viet-Thanh Pham, Thuy-Trang Vu, Junae Kim +4
Guardrail models are increasingly used as safety layers for LLMs and agentic systems, but most are trained on fixed prompt or response interfaces and become unreliable when the sam…
Stochastic Teacher Intervention for Agentic On-Policy Distillation
Junnan Liu, Linhao Luo, Zhijun Chen +3
On-policy distillation (OPD) efficiently transfers capabilities from a stronger teacher to a student language model through dense token-level supervision on student-generated rollo…
MATO: Multi-objective Personalized Alignment with Test-time Optimization for Large Language Models
Linhao Luo, Thuy-Trang Vu, Van-Anh Nguyen +3
Aligning large language models (LLMs) with diverse and multifaceted user preferences is a fundamental challenge in personalized AI systems. Existing multi-objective alignment metho…
GTS: Inference-Time Scaling of Latent Reasoning with a Learnable Gaussian Thought Sampler
Minghan Wang, Ye Bai, Thuy-Trang Vu +2
Inference-time scaling (ITS) in latent reasoning models typically relies on heuristic perturbations, such as dropout or fixed Gaussian noise, to generate diverse candidate trajecto…
CARD: Towards Conditional Design of Multi-agent Topological Structures
Tongtong Wu, Yanming Li, Ziye Tang +5
Large language model (LLM)-based multi-agent systems have shown strong capabilities in tasks such as code generation and collaborative reasoning. However, the effectiveness and rob…
IntroLM: Introspective Language Models via Prefilling-Time Self-Evaluation
Hossein Hosseini Kasnavieh, Gholamreza Haffari, Chris Leckie +1
A major challenge for the operation of large language models (LLMs) is how to predict whether a specific LLM will produce sufficiently high-quality output for a given query. Existi…
Towards Inference-time Scaling for Continuous Space Reasoning
Minghan Wang, Thuy-Trang Vu, Ehsan Shareghi +1
Inference-time scaling through multiple sample generation in combination with Process- or Outcome-Reward Model (PRM or ORM) re-ranking has proven effective for text-based reasoning…
IRIS: An Iterative and Integrated Framework for Verifiable Causal Discovery in the Absence of Tabular Data
Tao Feng, Lizhen Qu, Niket Tandon +1
Causal discovery is fundamental to scientific research, yet traditional statistical algorithms face significant challenges, including expensive data collection, redundant computati…
Zero-Shot Privacy-Aware Text Rewriting via Iterative Tree Search
Shuo Huang, Xingliang Yuan, Gholamreza Haffari +1
The increasing adoption of large language models (LLMs) in cloud-based services has raised significant privacy concerns, as user inputs may inadvertently expose sensitive informati…
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…
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…
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…
SituatedThinker: Grounding LLM Reasoning with Real-World through Situated Thinking
Junnan Liu, Linhao Luo, Thuy-Trang Vu +1
Recent advances in large language models (LLMs) demonstrate their impressive reasoning capabilities. However, the reasoning confined to internal parametric space limits LLMs' acces…
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…
RIDE: Enhancing Large Language Model Alignment through Restyled In-Context Learning Demonstration Exemplars
Yuncheng Hua, Lizhen Qu, Zhuang Li +3
Alignment tuning is crucial for ensuring large language models (LLMs) behave ethically and helpfully. Current alignment approaches require high-quality annotations and significant…
CONGRAD:Conflicting Gradient Filtering for Multilingual Preference Alignment
Jiangnan Li, Thuy-Trang Vu, Christian Herold +3
Naive joint training of large language models (LLMs) for multilingual preference alignment can suffer from negative interference. This is a known issue in multilingual training, wh…
Attending To Syntactic Information In Biomedical Event Extraction Via Graph Neural Networks
Farshad Noravesh, Reza Haffari, Ong Huey Fang +3
Many models are proposed in the literature on biomedical event extraction(BEE). Some of them use the shortest dependency path(SDP) information to represent the argument classificat…
Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation
Samin Mahdizadeh Sani, Pouya Sadeghi, Thuy-Trang Vu +2
Large language models (LLMs) have made great progress in classification and text generation tasks. However, they are mainly trained on English data and often struggle with low-reso…
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…
MADial-Bench: Towards Real-world Evaluation of Memory-Augmented Dialogue Generation
Junqing He, Liang Zhu, Rui Wang +3
Long-term memory is important for chatbots and dialogue systems (DS) to create consistent and human-like conversations, evidenced by numerous developed memory-augmented DS (MADS).…
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…
Scalable Frame-based Construction of Sociocultural NormBases for Socially-Aware Dialogues
Shilin Qu, Weiqing Wang, Xin Zhou +6
Sociocultural norms serve as guiding principles for personal conduct in social interactions, emphasizing respect, cooperation, and appropriate behavior, which is able to benefit ta…
Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models
Linhao Luo, Zicheng Zhao, Gholamreza Haffari +3
Large language models (LLMs) have demonstrated impressive reasoning abilities, but they still struggle with faithful reasoning due to knowledge gaps and hallucinations. To address…
Towards LifeSpan Cognitive Systems
Yu Wang, Chi Han, Tongtong Wu +9
Building a human-like system that continuously interacts with complex environments -- whether simulated digital worlds or human society -- presents several key challenges. Central…