4 citations · 9 across the 8 of their papers we have counts for
11 papers
Adaptation of Computer Use Agents to New Software with User Manuals
Chulhoon Yoon, Kyungri Park, Jinyeong Yun +3
Recent advances in multimodal foundation models have significantly improved the ability to operate real-world software through graphical user interfaces (GUIs) using visual observa…
A Tree-based RAG Framework for Evidence-Intensive QA via Adaptive Planning and Topology-Aware Evidence Gathering
Songeun Lee, Kyungjin Min, Injae Na +3
Recent structured RAG methods leverage tree- or graph-based reasoning structures to improve multi-hop QA. However, they face key limitations in evidence-intensive QA, where answeri…
Automatic Transmission for LLM Tiers: Optimizing Cost and Accuracy in Large Language Models
Injae Na, Keonwoong Noh, Woohwan Jung
LLM providers typically offer multiple LLM tiers, varying in performance and price. As NLP tasks become more complex and modularized, selecting the suitable LLM tier for each subta…
A Single Model Ensemble Framework for Neural Machine Translation using Pivot Translation
Seokjin Oh, Keonwoong Noh, Woohwan Jung
Despite the recent remarkable advances in neural machine translation, translation quality for low-resource language pairs remains subpar. Ensembling multiple systems is a widely ad…
Improving Detail in Pluralistic Image Inpainting with Feature Dequantization
Kyungri Park, Woohwan Jung
Pluralistic Image Inpainting (PII) offers multiple plausible solutions for restoring missing parts of images and has been successfully applied to various applications including ima…
Improving Domain-Specific ASR with LLM-Generated Contextual Descriptions
Jiwon Suh, Injae Na, Woohwan Jung
End-to-end automatic speech recognition (E2E ASR) systems have significantly improved speech recognition through training on extensive datasets. Despite these advancements, they st…
Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets
Su Ah Lee, Seokjin Oh, Woohwan Jung
Named Entity Recognition (NER) frequently suffers from the problem of insufficient labeled data, particularly in fine-grained NER scenarios. Although -shot learning techniques c…
Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models
Jaeyoung Choe, Keonwoong Noh, Nayeon Kim +2
Over the past few years, various domain-specific pretrained language models (PLMs) have been proposed and have outperformed general-domain PLMs in specialized areas such as biomedi…
Data Augmentation for Neural Machine Translation using Generative Language Model
Seokjin Oh, Su Ah Lee, Woohwan Jung
Despite the rapid growth in model architecture, the scarcity of large parallel corpora remains the main bottleneck in Neural Machine Translation. Data augmentation is a technique t…
Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation
Woohwan Jung, Kyuseok Shim
Relation extraction (RE) has been extensively studied due to its importance in real-world applications such as knowledge base construction and question answering. Most of the exist…
Crowdsourced Truth Discovery in the Presence of Hierarchies for Knowledge Fusion
Woohwan Jung, Younghoon Kim, Kyuseok Shim
Existing works for truth discovery in categorical data usually assume that claimed values are mutually exclusive and only one among them is correct. However, many claimed values ar…