200 citations · 223 across the 9 of their papers we have counts for
10 papers
LLMEffiChecker: Understanding and Testing Efficiency Degradation of Large Language Models
Xiaoning Feng, Xiaohong Han, Simin Chen +1
In this paper, we make the first attempt to understand and test potential computation efficiency robustness in state-of-the-art LLMs. By analyzing the working mechanism and impleme…
Hierarchical Neural Data Synthesis for Semantic Parsing
Wei Yang, Peng Xu, Yanshuai Cao
Semantic parsing datasets are expensive to collect. Moreover, even the questions pertinent to a given domain, which are the input of a semantic parsing system, might not be readily…
Estimating Predictive Uncertainty Under Program Data Distribution Shift
Yufei Li, Simin Chen, Wei Yang
Deep learning (DL) techniques have achieved great success in predictive accuracy in a variety of tasks, but deep neural networks (DNNs) are shown to produce highly overconfident sc…
SynthASR: Unlocking Synthetic Data for Speech Recognition
Amin Fazel, Wei Yang, Yulan Liu +4
End-to-end (E2E) automatic speech recognition (ASR) models have recently demonstrated superior performance over the traditional hybrid ASR models. Training an E2E ASR model require…
Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface
Peng Xu, Wenjie Zi, Hamidreza Shahidi +7
A natural language database interface (NLDB) can democratize data-driven insights for non-technical users. However, existing Text-to-SQL semantic parsers cannot achieve high enough…
A Globally Normalized Neural Model for Semantic Parsing
Chenyang Huang, Wei Yang, Yanshuai Cao +2
In this paper, we propose a globally normalized model for context-free grammar (CFG)-based semantic parsing. Instead of predicting a probability, our model predicts a real-valued s…