4 papers · 1 filter
DeTriever: Decoder-representation-based Retriever for Improving NL2SQL In-Context Learning
Yuxi Feng, Raymond Li, Zhenan Fan +4
While in-context Learning (ICL) has proven to be an effective technique to improve the performance of Large Language Models (LLMs) in a variety of complex tasks, notably in transla…
SQL-Encoder: Improving NL2SQL In-Context Learning Through a Context-Aware Encoder
Mohammadreza Pourreza, Davood Rafiei, Yuxi Feng +3
Detecting structural similarity between queries is essential for selecting examples in in-context learning models. However, assessing structural similarity based solely on the natu…
KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text Generation
Yuxi Feng, Xiaoyuan Yi, Laks V. S. Lakshmanan +1
Self-training (ST) has come to fruition in language understanding tasks by producing pseudo labels, which reduces the labeling bottleneck of language model fine-tuning. Nevertheles…
DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text Generation
Yuxi Feng, Xiaoyuan Yi, Xiting Wang +2
Self-training (ST) has prospered again in language understanding by augmenting the fine-tuning of pre-trained language models when labeled data is insufficient. However, it remains…