Search Results for author:
Found 7 papers, 2 papers with code
Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers
This paper presents an in-depth analysis of Large Language Models (LLMs), focusing on LLaMA, a prominent open-source foundational model in natural language processing.
Large Language Models are Diverse Role-Players for Summarization Evaluation
First, we propose to model objective and subjective dimensions of generated text based on roleplayers prompting mechanism.
Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding
Despite the great success of spoken language understanding (SLU) in high-resource languages, it remains challenging in low-resource languages mainly due to the lack of labeled training data.
Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition
Named entity recognition (NER) is a fundamental component in many applications, such as Web Search and Voice Assistants.
A Review of Dataset and Labeling Methods for Causality Extraction
Moreover, there is a lack of unified causal sequence label methods, which constitute the key factors that hinder the progress of causality extraction research.
CalibreNet: Calibration Networks for Multilingual Sequence Labeling
To tackle the challenge of lack of training data in low-resource languages, we dedicatedly develop a novel unsupervised phrase boundary recovery pre-training task to enhance the multilingual boundary detection capability of CalibreNet.
A Multi-level Neural Network for Implicit Causality Detection in Web Texts
Mining causality from text is a complex and crucial natural language understanding task corresponding to the human cognition.
