1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.SE2024
Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation
Seonghyeon Lee, Suyeon Kim, Joonwon Jang +3
We study the code generation behavior of instruction-tuned models built on top of code pre-trained language models when they could access an auxiliary function to implement a funct…
cs.SE2024
Exploring Language Model's Code Generation Ability with Auxiliary Functions
Seonghyeon Lee, Sanghwan Jang, Seongbo Jang +2
Auxiliary function is a helpful component to improve language model's code generation ability. However, a systematic exploration of how they affect has yet to be done. In this work…
cs.AI2024★ 1 cited
Improving Matrix Completion by Exploiting Rating Ordinality in Graph Neural Networks
Jaehyun Lee, SeongKu Kang, Hwanjo Yu
Matrix completion is an important area of research in recommender systems. Recent methods view a rating matrix as a user-item bi-partite graph with labeled edges denoting observed…