High-quality text embedding is pivotal in improving semantic textual similarity (STS) tasks,
which are crucial components in Large Language Model (LLM) applications. However, a …

Despite recent progress in improving the performance of misinformation detection systems,
classifying misinformation in an unseen domain remains an elusive challenge. To address …

Large language models (LLMs) are increasingly being used for generating text in a variety
of use cases, including journalistic news articles. Given the potential malicious nature in …

Current NLP models heavily rely on effective representation learning algorithms. Contrastive
learning is one such technique to learn an embedding space such that similar data sample …

This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …

With emerging online topics as a source for numerous new events, detecting unseen/rare
event types presents an elusive challenge for existing event detection methods, where only …

Question answering (QA) has demonstrated impressive progress in answering questions
from customized domains. Nevertheless, domain adaptation remains one of the most elusive …

Synthesizing QA pairs with a question generator (QG) on the target domain has become a
popular approach for domain adaptation of question answering (QA) models. Since …

Training large deep learning (DL) models with high performance for natural language
downstream tasks usually requires rich-labeled data. However, in a real-world application of …

We study learning from user feedback for extractive question answering by simulating
feedback using supervised data. We cast the problem as contextual bandit learning, and …