65 citations · 149 across the 12 of their papers we have counts for
12 papers
Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models
Ning Ding, Yulin Chen, Ganqu Cui +6
Underlying data distributions of natural language, programming code, and mathematical symbols vary vastly, presenting a complex challenge for large language models (LLMs) that stri…
Sparse Low-rank Adaptation of Pre-trained Language Models
Ning Ding, Xingtai Lv, Qiaosen Wang +4
Fine-tuning pre-trained large language models in a parameter-efficient manner is widely studied for its effectiveness and efficiency. The popular method of low-rank adaptation (LoR…
Empowering Private Tutoring by Chaining Large Language Models
Yulin Chen, Ning Ding, Hai-Tao Zheng +3
Artificial intelligence has been applied in various aspects of online education to facilitate teaching and learning. However, few approaches has been made toward a complete AI-powe…
Exploring Lottery Prompts for Pre-trained Language Models
Yulin Chen, Ning Ding, Xiaobin Wang +4
Consistently scaling pre-trained language models (PLMs) imposes substantial burdens on model adaptation, necessitating more efficient alternatives to conventional fine-tuning. Give…
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
Ning Ding, Yulin Chen, Bokai Xu +6
Fine-tuning on instruction data has been widely validated as an effective practice for implementing chat language models like ChatGPT. Scaling the diversity and quality of such dat…
MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction
Xiaozhi Wang, Yulin Chen, Ning Ding +9
The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two…
Few-shot Classification with Hypersphere Modeling of Prototypes
Ning Ding, Yulin Chen, Ganqu Cui +4
Metric-based meta-learning is one of the de facto standards in few-shot learning. It composes of representation learning and metrics calculation designs. Previous works construct c…
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
Ning Ding, Yujia Qin, Guang Yang +17
Despite the success, the process of fine-tuning large-scale PLMs brings prohibitive adaptation costs. In fact, fine-tuning all the parameters of a colossal model and retaining sepa…
OpenPrompt: An Open-source Framework for Prompt-learning
Ning Ding, Shengding Hu, Weilin Zhao +4
Prompt-learning has become a new paradigm in modern natural language processing, which directly adapts pre-trained language models (PLMs) to -style prediction, autoregressiv…
Prompt-Learning for Fine-Grained Entity Typing
Ning Ding, Yulin Chen, Xu Han +6
As an effective approach to tune pre-trained language models (PLMs) for specific tasks, prompt-learning has recently attracted much attention from researchers. By using \textit{clo…
Few-NERD: A Few-Shot Named Entity Recognition Dataset
Ning Ding, Guangwei Xu, Yulin Chen +5
Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practica…
High-throughput relation extraction algorithm development associating knowledge articles and electronic health records
Yucong Lin, Keming Lu, Yulin Chen +2
Objective: Medical relations are the core components of medical knowledge graphs that are needed for healthcare artificial intelligence. However, the requirement of expert annotati…