1 citations · 1 across the 11 of their papers we have counts for
11 papers
CIRF: Tokenizing Chain-of-Thoughts into Reusable Functional Units for Efficient Latent Reasoning in Large Language Models
Yukyung Lee, Yumeng Shen, Jinhyeong Park +2
Implicit Chain-of-Thought (CoT) reduces the inference cost of large language models by internalizing the explicit rationales. However, existing approaches typically lack alignment…
MolDA: Molecular Understanding and Generation via Large Language Diffusion Model
Seohyeon Shin, HanJun Choi, Jun-Hyung Park +2
Large Language Models (LLMs) have significantly advanced molecular discovery, but existing multimodal molecular architectures fundamentally rely on autoregressive (AR) backbones. T…
Handling Korean Out-of-Vocabulary Words with Phoneme Representation Learning
Nayeon Kim, Eojin Jeon, Jun-Hyung Park +1
In this study, we introduce KOPL, a novel framework for handling Korean OOV words with Phoneme representation Learning. Our work is based on the linguistic property of Korean as a…
Incorporating Domain Knowledge into Materials Tokenization
Yerim Oh, Jun-Hyung Park, Junho Kim +2
While language models are increasingly utilized in materials science, typical models rely on frequency-centric tokenization methods originally developed for natural language proces…
C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning
Yeachan Kim, Junho Kim, Wing-Lam Mok +2
Despite the versatility of pre-trained language models (PLMs) across domains, their large memory footprints pose significant challenges in federated learning (FL), where the traini…
MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Science
Junho Kim, Yeachan Kim, Jun-Hyung Park +3
We introduce a novel continued pre-training method, MELT (MatEriaLs-aware continued pre-Training), specifically designed to efficiently adapt the pre-trained language models (PLMs)…
Zero-shot Commonsense Reasoning over Machine Imagination
Hyuntae Park, Yeachan Kim, Jun-Hyung Park +1
Recent approaches to zero-shot commonsense reasoning have enabled Pre-trained Language Models (PLMs) to learn a broad range of commonsense knowledge without being tailored to speci…
DIVE: Towards Descriptive and Diverse Visual Commonsense Generation
Jun-Hyung Park, Hyuntae Park, Youjin Kang +2
Towards human-level visual understanding, visual commonsense generation has been introduced to generate commonsense inferences beyond images. However, current research on visual co…
MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction
Jun-Hyung Park, Yeachan Kim, Mingyu Lee +2
Chemical representation learning has gained increasing interest due to the limited availability of supervised data in fields such as drug and materials design. This interest partic…
Dynamic Structure Pruning for Compressing CNNs
Jun-Hyung Park, Yeachan Kim, Junho Kim +2
Structure pruning is an effective method to compress and accelerate neural networks. While filter and channel pruning are preferable to other structure pruning methods in terms of…
Efficient Pre-training of Masked Language Model via Concept-based Curriculum Masking
Mingyu Lee, Jun-Hyung Park, Junho Kim +2
Masked language modeling (MLM) has been widely used for pre-training effective bidirectional representations, but incurs substantial training costs. In this paper, we propose a nov…