10 citations · 27 across the 10 of their papers we have counts for
5 papers · 2 filters
DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models
Chengcheng Han, Xiaowei Du, Che Zhang +4
Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters. However, i…
When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario
Chengcheng Han, Liqing Cui, Renyu Zhu +5
Large pre-trained language models (PLMs) have garnered significant attention for their versatility and potential for solving a wide spectrum of natural language processing (NLP) ta…
Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectives
Qiushi Sun, Chengcheng Han, Nuo Chen +4
Large language models (LLMs) have shown increasing power on various natural language processing (NLP) tasks. However, tuning these models for downstream tasks usually needs exorbit…
Meta-Learning Siamese Network for Few-Shot Text Classification
Chengcheng Han, Yuhe Wang, Yingnan Fu +4
Few-shot learning has been used to tackle the problem of label scarcity in text classification, of which meta-learning based methods have shown to be effective, such as the prototy…
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition
Chengcheng Han, Renyu Zhu, Jun Kuang +5
Meta-learning methods have been widely used in few-shot named entity recognition (NER), especially prototype-based methods. However, the Other(O) class is difficult to be represent…