14 citations · 51 across the 19 of their papers we have counts for
6 papers · 2 filters
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning
Jianing Wang, Chengyu Wang, Chuanqi Tan +2
Large language models (LLMs) enable in-context learning (ICL) by conditioning on a few labeled training examples as a text-based prompt, eliminating the need for parameter updates…
TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification
Jianing Wang, Chengyu Wang, Cen Chen +3
Text classification is one of the most imperative tasks in natural language processing (NLP). Recent advances with pre-trained language models (PLMs) have shown remarkable success…
Boosting Language Models Reasoning with Chain-of-Knowledge Prompting
Jianing Wang, Qiushi Sun, Xiang Li +1
Recently, Chain-of-Thought (CoT) prompting has delivered success on complex reasoning tasks, which aims at designing a simple prompt like ``Let's think step by step'' or multiple 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…
HugNLP: A Unified and Comprehensive Library for Natural Language Processing
Jianing Wang, Nuo Chen, Qiushi Sun +3
In this paper, we introduce HugNLP, a unified and comprehensive library for natural language processing (NLP) with the prevalent backend of HuggingFace Transformers, which is desig…
Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling
Jianing Wang, Chengyu Wang, Jun Huang +2
Neural sequence labeling (NSL) aims at assigning labels for input language tokens, which covers a broad range of applications, such as named entity recognition (NER) and slot filli…