Papers by Jun Seo
Semiparametric Token-Sequence Co-Supervision (2024.acl-long)
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| Challenge: | Using semiparametric token-sequence co-supervision, language models are trained using a finite parametric vocabulary space. |
| Approach: | They propose a semiparametric token-sequence co-supervision training method that leverages supervision from two different supervisions. |
| Outcome: | The proposed method outperforms models trained via each supervision independently and shows that it encourages a broader generalization capability across the model. |
A Multi-Agent Framework for Feature-Constrained Difficulty Control in Reading Comprehension Item Generation (2026.acl-long)
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| Challenge: | Existing methods for difficulty-controlled reading comprehension item generation rely on a single agent prompting approach. |
| Approach: | They propose a multi-agent framework for Feature-constrained Item Generation where multiple LLM agents collaborate to generate and iteratively revise items based on intended constraints. |
| Outcome: | The proposed method generates items with monotonically increasing difficulty at higher rates than baselines. |
Behavior-Aware Item Modeling via Dynamic Procedural Solution Representations for Knowledge Tracing (2026.findings-acl)
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| Challenge: | Knowledge Tracing (KT) aims to predict learners’ future performance from past interactions, but they overlook the procedural dynamics of problem solving. |
| Approach: | They propose a framework that enriches item representations by integrating dynamic procedural solution information. |
| Outcome: | Experiments on XES3G5M and NIPS34 show that BAIM outperforms strong pretraining-based baselines, achieving particularly large gains under repeated learner interactions. |