Papers by Youngseo Son
Transfer Learning for Generalizable Automated LLM Improvement Pipeline for IVR Navigation (2026.acl-industry)
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| Challenge: | Using a human-labeled ground truth dataset, we can transfer specialized prompts from one task to another, reducing the complexity of the task. |
| Approach: | They propose a production-quality automated LLM pipeline which leverages a small number of human-labeled ground truth datasets to transfer specialized prompts from one task to another. |
| Outcome: | The proposed model reduces prompt complexity by up to 80% and obtains 82% turn-level accuracy in real-world industrial healthcare settings, surpassing a human-designed prompt at 79%. |
Auto Review: Second Stage Error Detection for Highly Accurate Information Extraction from Phone Conversations (2025.acl-industry)
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| Challenge: | Automating benefit verification phone calls saves time and improves patient care. |
| Approach: | They propose a second-stage postprocessing pipeline that reduces manual effort while maintaining a high bar for accuracy. |
| Outcome: | The proposed system significantly reduces manual effort while maintaining a high bar for accuracy while reducing noise and jargon. |
Causal Explanation Analysis on Social Media (D18-1)
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| Challenge: | Understanding causal explanations is an important psychological factor linked to physical and mental health. |
| Approach: | They propose to automate causal explanation analysis by building on discourse parsing and using a hierarchy of Bidirectional LSTMs to identify the specific phrase that is the explanation. |
| Outcome: | The proposed subtasks achieve strong accuracies but differ in their approaches . the proposed sub task is compared with the previous task and is able to identify the specific phrase that is the explanation. |
Author’s Sentiment Prediction (2020.coling-main)
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| Challenge: | Existing work on inferring author sentiment in news articles hasn't been done on this domain. |
| Approach: | They propose a crowd-sourced dataset that captures the sentiment of an author towards the main entity in a news article. |
| Outcome: | The proposed dataset performs the best amongst the baselines, but only achieves modest performance overall suggesting that fine-tuning document-level representations aloneisn’t adequate for this task. |
LingVarBench: Benchmarking LLMs on Entity Recognitions and Linguistic Verbalization Patterns in Phone-Call Transcripts (2026.eacl-industry)
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| Challenge: | Existing methods degrade under disfluencies, interruptions, and speaker overlap, yet large real-call corpora are rarely shareable. |
| Approach: | They propose a benchmark and semantic synthetic data generation pipeline that generates linguistically varied training data via (1) LLM-sampled entity values, (2) curated linguistic verbalization patterns covering diverse disfluencies and entity-specific readout styles, and (3) a value–transcript consistency filter. |
| Outcome: | The proposed pipeline outperforms zero-shot baselines and matches or closely approaches human-tuned prompts on real customer transcripts. |