24 citations · 38 across the 6 of their papers we have counts for
6 papers
SiRA: Sparse Mixture of Low Rank Adaptation
Yun Zhu, Nevan Wichers, Chu-Cheng Lin +8
Parameter Efficient Tuning has been an prominent approach to adapt the Large Language Model to downstream tasks. Most previous works considers adding the dense trainable parameters…
FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning
Xinyi Wang, John Wieting, Jonathan H. Clark
Learning paradigms for large language models (LLMs) currently tend to fall within either in-context learning (ICL) or full fine-tuning. Each of these comes with their own trade-off…
Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies
Liangming Pan, Michael Saxon, Wenda Xu +3
Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks. However, their efficacy is undermined by undesired and inconsistent behavior…
Non-parametric Probabilistic Time Series Forecasting via Innovations Representation
Xinyi Wang, Meijen Lee, Qing Zhao +1
Probabilistic time series forecasting predicts the conditional probability distributions of the time series at a future time given past realizations. Such techniques are critical i…
Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm
Xinyi Wang, Simon Yusuf Enoch, Dong Seong Kim
Widely used deep learning models are found to have poor robustness. Little noises can fool state-of-the-art models into making incorrect predictions. While there is a great deal of…
Gradient-guided Loss Masking for Neural Machine Translation
Xinyi Wang, Ankur Bapna, Melvin Johnson +1
To mitigate the negative effect of low quality training data on the performance of neural machine translation models, most existing strategies focus on filtering out harmful data b…