Papers by Seo Yeon Park
VerifyMatch: A Semi-Supervised Learning Paradigm for Natural Language Inference with Confidence-Aware MixUp (2024.emnlp-main)
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| Challenge: | Natural language inference (NLI) is a key task for evaluating a model's ability to perform natural language understanding and reasoning. |
| Approach: | They propose to construct pseudo-generated samples using class-specific fine-tuned large language models (LLMs) . they retain all pseudo-labeled samples, but use MixUp to ensure unlabele . |
| Outcome: | The proposed approach achieves competitive accuracy compared to strong baselines for NLI datasets in low-resource settings. |
On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency (2022.acl-long)
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| Challenge: | Existing studies have shown that mixing up can improve model calibration on image classification tasks, but little is known about using it on natural language understanding (NLU) tasks. |
| Approach: | They propose a mixup strategy for pre-trained language models that improves model calibration further by using the AUM statistic and saliency map. |
| Outcome: | The proposed mixup improves model calibration on natural language understanding tasks while maintaining competitive accuracy. |
Leveraging Moment Injection for Enhanced Semi-supervised Natural Language Inference with Large Language Models (2025.naacl-short)
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| Challenge: | Existing studies have used class-specific fine-tuned large language models to generate hypotheses and assign pseudo-labels but discarded many LLM-constructed samples to ensure the quality. |
| Approach: | They propose to leverage LLM-constructed samples by injecting the moments of labeled samples during training to properly adjust the level of noise. |
| Outcome: | The proposed method outperforms strong baselines on multiple NLI datasets in low-resource settings. |
PPA-Plan: Proactive Pitfall Avoidance for Reliable Planning in Long-Context LLM Reasoning (2026.acl-long)
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| Challenge: | Large language models struggle with reasoning over long contexts where relevant information is sparsely distributed. |
| Approach: | They propose a plan-and-execute framework that decomposes long-context tasks into an explicit planning stage followed by step-by-step step-through planning. |
| Outcome: | Experiments on long-context QA benchmarks show that PPA-Plan outperforms plan-and-execute methods and direct prompting. |
KPDROP: Improving Absent Keyphrase Generation (2022.findings-emnlp)
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| Challenge: | Existing approaches to generate absent keyphrases have limited results in generating present keyphrase. |
| Approach: | They propose a model-agnostic approach to generate absent keyphrases by randomly dropping present keyphrase from a document and turning them into artificial absent key phrases during training. |
| Outcome: | The proposed approach improves absent keyphrase generation in both supervised and resource-constrained settings. |
A Data Cartography based MixUp for Pre-trained Language Models (2022.naacl-main)
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| Challenge: | Using MixUp, additional samples are generated during training by combining random pairs of training samples and their labels. |
| Approach: | They propose a new MixUp strategy that leverages Training Dynamics and allows more informative samples to be combined for generating new data samples. |
| Outcome: | The proposed method achieves competitive performance using a smaller subset of training data compared with strong baselines and yields lower expected calibration error on the pre-trained language model, BERT, on both in-domain and out-of-domain settings. |