Papers by Woohwan Jung
Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models (2023.findings-emnlp)
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| Challenge: | Existing financial PLMs are not pretrained on sufficiently diverse financial data, leading to subpar generalization performance. |
| Approach: | They propose to pretrain financial PLMs on financial corpus and train financial models on financial data. |
| Outcome: | The proposed financial language models outperform existing financial PLMs on financial tasks even for unseen corpus groups. |
DP-FROST: Differentially Private Fine-tuning of Pre-trained Models with Freezing Model Parameters (2025.coling-main)
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| Challenge: | Training models with differential privacy has received a lot of attention since it provides theoretical guarantee of privacy preservation. |
| Approach: | They propose methods that fine-tune large-scale pre-trained models with freezing unimportant parameters for downstream tasks while satisfying differential privacy. |
| Outcome: | The proposed methods fine-tune large pre-trained models with freezing unimportant parameters while satisfying differential privacy while preserving their utility. |
Hierarchical Retrieval with Evidence Curation for Open-Domain Financial Question Answering on Standardized Documents (2025.findings-acl)
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| Challenge: | standardized documents share similar formats and table structures . this similarity forces traditional RAG methods to misidentify near-duplicate text . |
| Approach: | They propose a hierarchical retrieval framework that performs hierarchically to reduce confusion among similar texts. |
| Outcome: | The proposed framework reduces confusion among similar documents by removing irrelevant passages . it generates complementary queries to collect missing information . |
Automatic Transmission for LLM Tiers: Optimizing Cost and Accuracy in Large Language Models (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are powerful tools for a wide range of natural language tasks. |
| Approach: | They propose an LLM automatic transmission framework that automatically selects LLM tiers without training. |
| Outcome: | The proposed framework achieves superior performance while reducing costs. |
Beyond Reference: Evaluating High Quality Translations Better than Human References (2024.emnlp-main)
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| Challenge: | Existing machine translation metrics give maximum score to reference sentence . however, these metrics overlook the possibility that candidate sentences outperform reference sentences in terms of quality. |
| Approach: | They propose a machine translation metrics that give an absolute score to a translated sentence based on the similarity with the reference sentence. |
| Outcome: | The proposed measure outperforms existing MT metrics in terms of quality and assigns positive scores to candidates that outperformed reference sentences. |
Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets (2023.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) often suffers from insufficient labeled data when the number of annotations exceeds several tens of labels. |
| Approach: | They propose a model with a fine-to- coarse mapping matrix to leverage hierarchical structure explicitly. |
| Outcome: | The proposed model outperforms both K-shot learning and supervised learning methods when dealing with a small number of fine-grained annotations. |
Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation (2020.coling-main)
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| Challenge: | Existing studies on relation extraction (RE) use labeled training data for relation extraction models but it is expensive and time-consuming. |
| Approach: | They propose a dual supervision framework which utilizes both types of data to train relation extraction models. |
| Outcome: | The proposed framework can predict labels by human annotation and distant supervision without labeling bias since it is expensive and time-consuming. |