Leveraging large language models for enhanced nlp task performance through knowledge distillation and optimized training strategies
Y Huang, K Tang, M Chen - arXiv preprint arXiv:2402.09282, 2024 - arxiv.org
Emerging Large Language Models (LLMs) like GPT-4 have revolutionized Natural
Language Processing (NLP), showing potential in traditional tasks such as Named Entity …
Language Processing (NLP), showing potential in traditional tasks such as Named Entity …
C-icl: Contrastive in-context learning for information extraction
Recently, there has been increasing interest in exploring the capabilities of advanced large
language models (LLMs) in the field of information extraction (IE), specifically focusing on …
language models (LLMs) in the field of information extraction (IE), specifically focusing on …
New Intent Discovery with Attracting and Dispersing Prototype
New Intent Discovery (NID) aims to recognize known and infer new intent categories with the
help of limited labeled and large-scale unlabeled data. The task is addressed as a feature …
help of limited labeled and large-scale unlabeled data. The task is addressed as a feature …
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems
Cascade ranking is widely used for large-scale top-k selection problems in online
advertising and recommendation systems, and learning-to-rank is an important way to …
advertising and recommendation systems, and learning-to-rank is an important way to …
XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser
X Cheng, H Zhang, J Yang, X Li, W Zhou, K Wu… - arXiv preprint arXiv …, 2024 - arxiv.org
In the domain of document AI, semi-structured form parsing plays a crucial role. This task
leverages techniques from key information extraction (KIE), dealing with inputs that range …
leverages techniques from key information extraction (KIE), dealing with inputs that range …
Incorporating Entity Type-Aware and Word–Word Relation-Aware Attention in Generative Named Entity Recognition
Y Mo, Z Li - Electronics, 2024 - mdpi.com
Named entity recognition (NER) is a critical subtask in natural language processing. It is
particularly valuable to gain a deeper understanding of entity boundaries and entity types …
particularly valuable to gain a deeper understanding of entity boundaries and entity types …
SEvenLLM: Benchmarking, Eliciting, and Enhancing Abilities of Large Language Models in Cyber Threat Intelligence
To address the increasing complexity and frequency of cybersecurity incidents emphasized
by the recent cybersecurity threat reports with over 10 billion instances, cyber threat …
by the recent cybersecurity threat reports with over 10 billion instances, cyber threat …
RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations
New Intent Discovery (NID) strives to identify known and reasonably deduce novel intent
groups in the open-world scenario. But current methods face issues with inaccurate pseudo …
groups in the open-world scenario. But current methods face issues with inaccurate pseudo …
Zero-Shot Cross-Lingual NER Using Phonemic Representations for Low-Resource Languages
Existing zero-shot cross-lingual NER approaches require substantial prior knowledge of the
target language, which is impractical for low-resource languages. In this paper, we propose …
target language, which is impractical for low-resource languages. In this paper, we propose …