In recent years, large language models (LLMs) have been widely adopted in political
science tasks such as election prediction, sentiment analysis, policy impact assessment, and …

Causal reasoning is a cornerstone of how humans interpret the world. To model and reason
about causality, causal graphs offer a concise yet effective solution. Given the impressive …

Recent breakthroughs in artificial intelligence have driven a paradigm shift, where large
language models (LLMs) with billions or trillions of parameters are trained on vast datasets …

Causal reasoning is the primary bottleneck that Large Language Models (LLMs) must
overcome to attain human-level intelligence. To address this, we introduce the Causal Chain …

Event causality identification (ECI), a process that extracts causal relations between events
from text, is crucial for distinguishing causation from correlation. Traditional approaches to …

Towards the goal of understanding the causal structure underlying complex systems-such
as the Earth, the climate, or the brain-integrating Large Language Models (LLMs) with data …

Affective artificial intelligence and multimodal sentiment analysis play critical roles in
designing safe and effective human-computer interactions and are in diverse applications …

Despite the increasing effectiveness of language models, their reasoning capabilities
remain underdeveloped. In particular, causal reasoning through counterfactual question …

Quantifying the effect of textual interventions in social systems, such as reducing anger in
social media posts to see its impact on engagement, poses significant challenges. Direct …

Large language models (LLMs) have excelled in various natural language processing tasks,
but challenges in interpretability and trustworthiness persist, limiting their use in high-stakes …