Papers by Mun Yong Yi
Leveraging LLM-Generated Schema Descriptions for Unanswerable Question Detection in Clinical Data (2025.coling-main)
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| Challenge: | Existing methods rely on model uncertainty but lack interpretability and data imbalance. |
| Approach: | They propose a lightweight model that predicts relevant database schemas to detect unanswerable questions, enhancing interpretability and addressing the data imbalance in binary classification tasks. |
| Outcome: | The proposed model improves interpretability and improves accuracy in binary classification tasks. |
FeedEval: Pedagogically Aligned Evaluation of LLM-Generated Essay Feedback (2026.findings-acl)
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| Challenge: | Recent research emphasizes the generation of high-quality feedback that provides justification and actionable guidance. |
| Approach: | They propose an LLM-based framework for evaluating LLM feedback along three dimensions: specificity, helpfulness, and validity. |
| Outcome: | The proposed framework evaluates LLM-generated feedback along three dimensions: specificity, helpfulness, and validity. |
Distilling LLM Reasoning into Dense Encoders: Bridging the Accuracy-Efficiency Gap in Recommendation (2026.findings-acl)
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| Challenge: | Existing distillation approaches target Small Language Models (SLMs) or Conventional Recommendation Models, but face a critical trade-off between computational cost and semantic reasoning capacity. |
| Approach: | They propose a framework that establishes a text encoder as the optimal student architecture for scalable recommendation. |
| Outcome: | Experiments on four datasets show that the proposed framework outperforms state-of-the-art models and achieves significantly reduced latency. |
Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have strong reasoning and generalization abilities, but they struggle to reflect the histories of example learners within a single prompt during in-context learning. |
| Approach: | They propose a LLM-based option weighted knowledge tracing framework that encodes the interaction histories of example learners in context as textual categorical option weights. |
| Outcome: | The proposed framework outperforms existing LLM-based KT models in warm-start and few-shot settings. |
Rationale Behind Essay Scores: Enhancing S-LLM’s Multi-Trait Essay Scoring with Rationale Generated by LLMs (2025.findings-naacl)
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| Challenge: | Existing automated essay scoring relies on essay text without explanatory rationales for the scores. |
| Approach: | They propose a rationale-based multiple trait scoring approach that integrates large language models with a smaller large language model to generate trait-specific rationales. |
| Outcome: | The proposed approach outperforms state-of-the-art models and vanilla S-LLMs on benchmark datasets. |
Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration (2025.findings-emnlp)
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| Challenge: | Query-to-Recommendation framework integrates large langucage models into recommendation systems . but it faces training-induced bias and bottlenecks from serialized architecture . |
| Approach: | They propose a parallel recommendation framework that decouples LLMs from candidate pre-selection and direct retrieval over the entire item pool. |
| Outcome: | The proposed framework decouples LLMs from candidate pre-selection and enables direct retrieval over the entire item pool. |