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Found 9 papers, 3 papers with code
Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity
Multi-task auxiliary learning utilizes a set of relevant auxiliary tasks to improve the performance of a primary task.
GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling
In this paper, we propose GenEARL, a training-free generative framework that harness the power of the modern generative models to understand event task descriptions given image contexts to perform the EARL task.
Improving Event Definition Following For Zero-Shot Event Detection
We hypothesize that a diverse set of event types and definitions are the key for models to learn to follow event definitions while existing event extraction datasets focus on annotating many high-quality examples for a few event types.
Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Tasks
Instruction tuning (IT) achieves impressive zero-shot generalization results by training large language models (LLMs) on a massive amount of diverse tasks with instructions.
MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways
We present MIDDAG, an intuitive, interactive system that visualizes the information propagation paths on social media triggered by COVID-19-related news articles accompanied by comprehensive insights, including user/community susceptibility level, as well as events and popular opinions raised by the crowd while propagating the information.
STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Models
Information extraction tasks such as event extraction require an in-depth understanding of the output structure and sub-task dependencies.
Do Models Really Learn to Follow Instructions? An Empirical Study of Instruction Tuning
Our experiments show that models trained on simplified task definition or delusive examples can achieve comparable performance to the ones trained on the original instructions and examples.
Multi-Task Learning for Situated Multi-Domain End-to-End Dialogue Systems
Task-oriented dialogue systems have been a promising area in the NLP field.
Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering
Extracting rationales can help human understand which information the model utilizes and how it makes the prediction towards better interpretability.
