162 citations · 187 across the 9 of their papers we have counts for
9 papers
Towards Zero-Shot Functional Compositionality of Language Models
Hangyeol Yu, Myeongho Jeong, Jamin Shin +3
Large Pre-trained Language Models (PLM) have become the most desirable starting point in the field of NLP, as they have become remarkably good at solving many individual tasks. Des…
Evaluating the Knowledge Dependency of Questions
Hyeongdon Moon, Yoonseok Yang, Jamin Shin +6
The automatic generation of Multiple Choice Questions (MCQ) has the potential to reduce the time educators spend on student assessment significantly. However, existing evaluation m…
Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking
Jamin Shin, Hangyeol Yu, Hyeongdon Moon +2
Annotating task-oriented dialogues is notorious for the expensive and difficult data collection process. Few-shot dialogue state tracking (DST) is a realistic solution to this prob…
Rethinking Task Sampling for Few-shot Vision-Language Transfer Learning
Zhenhailong Wang, Hang Yu, Manling Li +2
Despite achieving state-of-the-art zero-shot performance, existing vision-language models still fall short of few-shot transfer ability on domain-specific problems. Classical fine-…
Knowledge Transfer by Discriminative Pre-training for Academic Performance Prediction
Byungsoo Kim, Hangyeol Yu, Dongmin Shin +1
The needs for precisely estimating a student's academic performance have been emphasized with an increasing amount of attention paid to Intelligent Tutoring System (ITS). However,…
SAINT+: Integrating Temporal Features for EdNet Correctness Prediction
Dongmin Shin, Yugeun Shim, Hangyeol Yu +3
We propose SAINT+, a successor of SAINT which is a Transformer based knowledge tracing model that separately processes exercise information and student response information. Follow…
On Correctness of Automatic Differentiation for Non-Differentiable Functions
Wonyeol Lee, Hangyeol Yu, Xavier Rival +1
Differentiation lies at the core of many machine-learning algorithms, and is well-supported by popular autodiff systems, such as TensorFlow and PyTorch. Originally, these systems h…
Towards Verified Stochastic Variational Inference for Probabilistic Programs
Wonyeol Lee, Hangyeol Yu, Xavier Rival +1
Probabilistic programming is the idea of writing models from statistics and machine learning using program notations and reasoning about these models using generic inference engine…
Reparameterization Gradient for Non-differentiable Models
Wonyeol Lee, Hangyeol Yu, Hongseok Yang
We present a new algorithm for stochastic variational inference that targets at models with non-differentiable densities. One of the key challenges in stochastic variational infere…