52 citations · 159 across the 24 of their papers we have counts for
7 papers · 1 filter
CAMO: Causality-Guided Adversarial Multimodal Domain Generalization for Crisis Classification
Pingchuan Ma, Chengshuai Zhao, Bohan Jiang +5
Crisis classification in social media aims to extract actionable disaster-related information from multimodal posts, which is a crucial task for enhancing situational awareness and…
Who's Your Judge? On the Detectability of LLM-Generated Judgments
Dawei Li, Zhen Tan, Chengshuai Zhao +6
Large Language Model (LLM)-based judgments leverage powerful LLMs to efficiently evaluate candidate content and provide judgment scores. However, the inherent biases and vulnerabil…
An Interventional Approach to Real-Time Disaster Assessment via Causal Attribution
Saketh Vishnubhatla, Alimohammad Beigi, Rui Heng Foo +5
Traditional disaster analysis and modelling tools for assessing the severity of a disaster are predictive in nature. Based on the past observational data, these tools prescribe how…
Assessing On-the-Ground Disaster Impact Using Online Data Sources
Saketh Vishnubhatla, Ujun Jeong, Bohan Jiang +4
Assessing the impact of a disaster in terms of asset losses and human casualties is essential for preparing effective response plans. Traditional methods include offline assessment…
Are Today's LLMs Ready to Explain Well-Being Concepts?
Bohan Jiang, Dawei Li, Zhen Tan +2
Well-being encompasses mental, physical, and social dimensions essential to personal growth and informed life decisions. As individuals increasingly consult Large Language Models (…
Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens
Chengshuai Zhao, Zhen Tan, Pingchuan Ma +5
Chain-of-Thought (CoT) prompting has been shown to be effective in eliciting structured reasoning (i.e., CoT reasoning) from large language models (LLMs). Regardless of its popular…
Preference Leakage: A Contamination Problem in LLM-as-a-judge
Dawei Li, Renliang Sun, Yue Huang +6
Large Language Models (LLMs) as judges and LLM-based data synthesis have emerged as two fundamental LLM-driven data annotation methods in model development. While their combination…