4 citations · 6 across the 8 of their papers we have counts for
8 papers
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…
ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning
Vy Vo, Lizhen Qu, Tao Feng +6
Identifying cause-and-effect relationships is critical to understanding real-world dynamics and ultimately causal reasoning. Existing methods for identifying event causality in NLP…
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…
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…
RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations
Haolan Zhan, Zhuang Li, Xiaoxi Kang +14
Norm violations occur when individuals fail to conform to culturally accepted behaviors, which may lead to potential conflicts. Remediating norm violations requires social awarenes…
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…