10 citations · 24 across the 18 of their papers we have counts for
19 papers
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation
Menglong Lu, Zhen Huang, Yunxiang Zhao +3
Self-training emerges as an important research line on domain adaptation. By taking the model's prediction as the pseudo labels of the unlabeled data, self-training bootstraps the…
Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification
Menglong Lu, Zhen Huang, Zhiliang Tian +3
Text classification is a fundamental task for natural language processing, and adapting text classification models across domains has broad applications. Self-training generates ps…
Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models
Qingyue Wang, Yanhe Fu, Yanan Cao +3
Recently, large language models (LLMs), such as GPT-4, stand out remarkable conversational abilities, enabling them to engage in dynamic and contextually relevant dialogues across…
EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization
Peijie Dong, Lujun Li, Zimian Wei +3
Mixed-Precision Quantization~(MQ) can achieve a competitive accuracy-complexity trade-off for models. Conventional training-based search methods require time-consuming candidate tr…
Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain
Chunxi Guo, Zhiliang Tian, Jintao Tang +4
Text-to-SQL aims at generating SQL queries for the given natural language questions and thus helping users to query databases. Prompt learning with large language models (LLMs) has…
Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks
Haoqi Zheng, Qihuang Zhong, Liang Ding +4
Text classification tasks often encounter few shot scenarios with limited labeled data, and addressing data scarcity is crucial. Data augmentation with mixup has shown to be effect…