1 citations · 2 across the 9 of their papers we have counts for
9 papers
Twin Worlds: Equivariance-Based Abstention for Evidence-Grounded Reasoning
Vy Nguyen, Ziqi Xu, Jeffrey Chan +5
Knowledge-intensive reasoning requires Large Language Models (LLMs) to ground answers in provided evidence. When evidence is insufficient, it is desirable that models abstain rathe…
Debiasing Large Language Models via Adaptive Causal Prompting with Sketch-of-Thought
Bowen Li, Ziqi Xu, Jing Ren +5
Despite notable advancements in prompting methods for Large Language Models (LLMs), such as Chain-of-Thought (CoT), existing strategies still suffer from excessive token usage and…
Hallucinate Less by Thinking More: Aspect-Based Causal Abstention for Large Language Models
Vy Nguyen, Ziqi Xu, Jeffrey Chan +3
Large Language Models (LLMs) often produce fluent but factually incorrect responses, a phenomenon known as hallucination. Abstention, where the model chooses not to answer and inst…
Unbiased Reasoning for Knowledge-Intensive Tasks in Large Language Models via Conditional Front-Door Adjustment
Bo Zhao, Yinghao Zhang, Ziqi Xu +5
Large Language Models (LLMs) have shown impressive capabilities in natural language processing but still struggle to perform well on knowledge-intensive tasks that require deep rea…
Cultural Bias Matters: A Cross-Cultural Benchmark Dataset and Sentiment-Enriched Model for Understanding Multimodal Metaphors
Senqi Yang, Dongyu Zhang, Jing Ren +5
Metaphors are pervasive in communication, making them crucial for natural language processing (NLP). Previous research on automatic metaphor processing predominantly relies on trai…
Teaching Large Language Models Number-Focused Headline Generation With Key Element Rationales
Zhen Qian, Xiuzhen Zhang, Xiaofei Xu +1
Number-focused headline generation is a summarization task requiring both high textual quality and precise numerical accuracy, which poses a unique challenge for Large Language Mod…
Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning
Shuo Yu, Yingbo Wang, Ruolin Li +7
Graphs are data structures used to represent irregular networks and are prevalent in numerous real-world applications. Previous methods directly model graph structures and achieve…
FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks
Renqiang Luo, Huafei Huang, Shuo Yu +4
Fairness-aware Graph Neural Networks (GNNs) often face a challenging trade-off, where prioritizing fairness may require compromising utility. In this work, we re-examine fairness t…
FairGT: A Fairness-aware Graph Transformer
Renqiang Luo, Huafei Huang, Shuo Yu +2
The design of Graph Transformers (GTs) generally neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups. Since GTs encode graph info…