6 papers
LEAF: A Living Benchmark for Event-Augmented Forecasting
Mingtian Tan, Mihir Parmar, Palash Goyal +5
Large Language Models (LLMs) are increasingly applied to forecasting. To evaluate this capability while mitigating pre-training data contamination, several living benchmarks have b…
SNaRe: Domain-aware Data Generation for Low-Resource Event Detection
Tanmay Parekh, Yuxuan Dong, Lucas Bandarkar +4
Event Detection (ED) -- the task of identifying event mentions from natural language text -- is critical for enabling reasoning in highly specialized domains such as biomedicine, l…
ARMADA: Attribute-Based Multimodal Data Augmentation
Xiaomeng Jin, Jeonghwan Kim, Yu Zhou +4
In Multimodal Language Models (MLMs), the cost of manually annotating high-quality image-text pair data for fine-tuning and alignment is extremely high. While existing multimodal d…
CaLM: Contrasting Large and Small Language Models to Verify Grounded Generation
I-Hung Hsu, Zifeng Wang, Long T. Le +4
Grounded generation aims to equip language models (LMs) with the ability to produce more credible and accountable responses by accurately citing verifiable sources. However, existi…
GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling
Hritik Bansal, Po-Nien Kung, P. Jeffrey Brantingham +2
Multimodal event argument role labeling (EARL), a task that assigns a role for each event participant (object) in an image is a complex challenge. It requires reasoning over the en…
Argument-Aware Approach To Event Linking
I-Hung Hsu, Zihan Xue, Nilay Pochh +4
Event linking connects event mentions in text with relevant nodes in a knowledge base (KB). Prior research in event linking has mainly borrowed methods from entity linking, overloo…