2 citations · 3 across the 13 of their papers we have counts for
13 papers
Why It Hurts: Identifying the Drivers of Negative Thoughts in Emotional Support Conversations
Hainiu Xu, Zhaoyue Sun, Hanqi Yan +3
Large Language Models (LLMs) are increasingly used for emotional support tasks, such as negative thought reframing. This task relies on modifying cognitive appraisals, the subjecti…
HyLaT: Efficient Multi-Agent Communication via Hybrid Latent-Text Protocol
Xinyi Mou, Siyuan Wang, Zejun Li +2
Communication protocol design is a central challenge in large language model-based multi-agent systems. Existing single-channel approaches face an inherent communication trilemma:…
CAREBench: Evaluating LLMs' Emotion Understanding by Assessing Cognitive Appraisal Reasoning
Zhaoyue Sun, Hainiu Xu, Andero Uusberg +3
Emotion understanding is a core capability for LLMs to interact effectively with humans, yet existing evaluation paradigms rely on discrete emotion label prediction and fail to cap…
Modeling Subjectivity in Cognitive Appraisal with Language Models
Yuxiang Zhou, Hainiu Xu, Desmond C. Ong +3
As the utilization of language models in interdisciplinary, human-centered studies grow, expectations of their capabilities continue to evolve. Beyond excelling at conventional tas…
Causal Fine-Tuning under Latent Confounded Shift
Jialin Yu, Yuxiang Zhou, Haoxuan Li +6
Adapting to latent confounded shift remains a core challenge in modern AI. This setting is driven by hidden variables that induce spurious correlations between inputs and outputs d…
ExDDI: Explaining Drug-Drug Interaction Predictions with Natural Language
Zhaoyue Sun, Jiazheng Li, Gabriele Pergola +1
Predicting unknown drug-drug interactions (DDIs) is crucial for improving medication safety. Previous efforts in DDI prediction have typically focused on binary classification or p…
DrugWatch: A Comprehensive Multi-Source Data Visualisation Platform for Drug Safety Information
Artem Bobrov, Domantas Saltenis, Zhaoyue Sun +2
Drug safety research is crucial for maintaining public health, often requiring comprehensive data support. However, the resources currently available to the public are limited and…
Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring
Jiazheng Li, Hainiu Xu, Zhaoyue Sun +4
Generating rationales that justify scoring decisions has been a promising way to facilitate explainability in automated scoring systems. However, existing methods do not match the…
Cascading Large Language Models for Salient Event Graph Generation
Xingwei Tan, Yuxiang Zhou, Gabriele Pergola +1
Generating event graphs from long documents is challenging due to the inherent complexity of multiple tasks involved such as detecting events, identifying their relationships, and…
Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generation
Xingwei Tan, Yuxiang Zhou, Gabriele Pergola +1
Event temporal graphs have been shown as convenient and effective representations of complex temporal relations between events in text. Recent studies, which employ pre-trained lan…
Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study
Zhaoyue Sun, Gabriele Pergola, Byron C. Wallace +1
With the advent of large language models (LLMs), there has been growing interest in exploring their potential for medical applications. This research aims to investigate the abilit…
Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives
Runcong Zhao, Qinglin Zhu, Hainiu Xu +4
Existing datasets for narrative understanding often fail to represent the complexity and uncertainty of relationships in real-life social scenarios. To address this gap, we introdu…
Causal Inference from Text: Unveiling Interactions between Variables
Yuxiang Zhou, Yulan He
Adjusting for latent covariates is crucial for estimating causal effects from observational textual data. Most existing methods only account for confounding covariates that affect…