9 citations · 31 across the 34 of their papers we have counts for
15 papers · 1 filter
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…
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…
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…
CausalScore: An Automatic Reference-Free Metric for Assessing Response Relevance in Open-Domain Dialogue Systems
Tao Feng, Lizhen Qu, Xiaoxi Kang +1
Automatically evaluating the quality of responses in open-domain dialogue systems is a challenging but crucial task. Current evaluation metrics often fail to align with human judgm…
Causal Discovery Inspired Unsupervised Domain Adaptation for Emotion-Cause Pair Extraction
Yuncheng Hua, Yujin Huang, Shuo Huang +5
This paper tackles the task of emotion-cause pair extraction in the unsupervised domain adaptation setting. The problem is challenging as the distributions of the events causing em…
SCAR: Data Selection via Style Consistency-Aware Response Ranking for Efficient Instruction-Tuning of Large Language Models
Zhuang Li, Yuncheng Hua, Thuy-Trang Vu +3
Recent studies emphasize that manually ensuring a consistent response style and maintaining high data quality in training sets can significantly improve the performance of fine-tun…
Mixture-of-Skills: Learning to Optimize Data Usage for Fine-Tuning Large Language Models
Minghao Wu, Thuy-Trang Vu, Lizhen Qu +1
Large language models (LLMs) are typically fine-tuned on diverse and extensive datasets sourced from various origins to develop a comprehensive range of skills, such as writing, re…
NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human
Shuo Huang, William MacLean, Xiaoxi Kang +5
The widespread use of cloud-based Large Language Models (LLMs) has heightened concerns over user privacy, as sensitive information may be inadvertently exposed during interactions…
Revisiting Deep Audio-Text Retrieval Through the Lens of Transportation
Manh Luong, Khai Nguyen, Nhat Ho +3
The Learning-to-match (LTM) framework proves to be an effective inverse optimal transport approach for learning the underlying ground metric between two sources of data, facilitati…
IMO: Greedy Layer-Wise Sparse Representation Learning for Out-of-Distribution Text Classification with Pre-trained Models
Tao Feng, Lizhen Qu, Zhuang Li +3
Machine learning models have made incredible progress, but they still struggle when applied to examples from unseen domains. This study focuses on a specific problem of domain gene…
Let's Negotiate! A Survey of Negotiation Dialogue Systems
Haolan Zhan, Yufei Wang, Tao Feng +7
Negotiation is a crucial ability in human communication. Recently, there has been a resurgent research interest in negotiation dialogue systems, whose goal is to create intelligent…
Assistive Large Language Model Agents for Socially-Aware Negotiation Dialogues
Yuncheng Hua, Lizhen Qu, Gholamreza Haffari
We develop assistive agents based on Large Language Models (LLMs) that aid interlocutors in business negotiations. Specifically, we simulate business negotiations by letting two LL…
SADAS: A Dialogue Assistant System Towards Remediating Norm Violations in Bilingual Socio-Cultural Conversations
Yuncheng Hua, Zhuang Li, Linhao Luo +8
In today's globalized world, bridging the cultural divide is more critical than ever for forging meaningful connections. The Socially-Aware Dialogue Assistant System (SADAS) is our…
Importance-Aware Data Augmentation for Document-Level Neural Machine Translation
Minghao Wu, Yufei Wang, George Foster +2
Document-level neural machine translation (DocNMT) aims to generate translations that are both coherent and cohesive, in contrast to its sentence-level counterpart. However, due to…
Adapting Large Language Models for Document-Level Machine Translation
Minghao Wu, Thuy-Trang Vu, Lizhen Qu +2
Large language models (LLMs) have significantly advanced various natural language processing (NLP) tasks. Recent research indicates that moderately-sized LLMs often outperform larg…