1 citations · 1 across the 4 of their papers we have counts for
4 papers
60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering
Junjie Ye, Yuming Yang, Qi Zhang +5
Large language models (LLMs) encode extensive world knowledge through pre-training on massive datasets, which can then be fine-tuned for the question-answering (QA) task. However,…
Disentangled Neural Architecture Search
Xinyue Zheng, Peng Wang, Qigang Wang +1
Neural architecture search has shown its great potential in various areas recently. However, existing methods rely heavily on a black-box controller to search architectures, which…
Challenge Closed-book Science Exam: A Meta-learning Based Question Answering System
Xinyue Zheng, Peng Wang, Qigang Wang +1
Prior work in standardized science exams requires support from large text corpus, such as targeted science corpus fromWikipedia or SimpleWikipedia. However, retrieving knowledge fr…
Efficient Automatic Meta Optimization Search for Few-Shot Learning
Xinyue Zheng, Peng Wang, Qigang Wang +2
Previous works on meta-learning either relied on elaborately hand-designed network structures or adopted specialized learning rules to a particular domain. We propose a universal f…