Search Results for author:
Found 8 papers, 3 papers with code
Separating Hate Speech and Offensive Language Classes via Adversarial Debiasing
Research to tackle hate speech plaguing online media has made strides in providing solutions, analyzing bias and curating data.
GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism
Pretrained Language Models (PLMs) benefit from external knowledge stored in graph structures for various downstream tasks.
Decomposed Prompting: Unveiling Multilingual Linguistic Structure Knowledge in English-Centric Large Language Models
Despite the predominance of English in their training data, English-centric Large Language Models (LLMs) like GPT-3 and LLaMA display a remarkable ability to perform multilingual tasks, raising questions about the depth and nature of their cross-lingual capabilities.
GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network
Large Language Models (LLMs) exhibit strong In-Context Learning (ICL) capabilities when prompts with demonstrations are applied to them.
Why Lift so Heavy? Slimming Large Language Models by Cutting Off the Layers
Large Language Models (LLMs) possess outstanding capabilities in addressing various natural language processing (NLP) tasks.
ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks
However, most previous studies primarily focused on sentence-level classification tasks, and only a few considered token-level labeling tasks such as Named Entity Recognition (NER) and Part-of-Speech (POS) tagging.
Evaluating Generative Models for Graph-to-Text Generation
Large language models (LLMs) have been widely employed for graph-to-text generation tasks.
Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigation
With the remarkable increase in the number of scientific entities such as publications, researchers, and scientific topics, and the associated information overload in science, academic recommender systems have become increasingly important for millions of researchers and science enthusiasts.
