Papers by Hyeon-Tae Seo

2 papers
PSYDIAL: Personality-based Synthetic Dialogue Generation Using Large Language Models (2024.lrec-main)

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Challenge: a new pipeline for personality-based synthetic dialogues is being developed in Korea . a dataset curated by large language models is needed to generate human-like dialogues .
Approach: They propose a personality-based synthetic dialogue data pipeline to elicit responses from large language models via prompting.
Outcome: The proposed pipeline generates human-like dialogues considering real-world scenarios when users engage with chatbots.
SuperST: Superficial Self-Training for Few-Shot Text Classification (2024.lrec-main)

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Challenge: In few-shot text classification, self-training relies on pseudo-labels to expand data, which has shown success, but can accumulate errors due to noisy pseudo-labeled data.
Approach: They propose a method to mitigate noise in noisy pseudo-labeled data by applying superficial learning to noisy data and fine-tuning to less noisy data.
Outcome: The proposed framework improves the classifier accuracy for few-shot text classification by 18.5% at most and 8% in average, compared with the state-of-the-art SSL baselines.

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