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Jun Seo

ACL 2026×2智能体与工具评测与基准可解释性推理对齐与偏好
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一作贡献30
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1 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
作者位次 #2/4ACL 2026Poster· Natural Language Processing

A Multi-Agent Framework for Feature-Constrained Difficulty Control in Reading Comprehension Item Generation

Seonjeong Hwang · Jun Seo · Hyounghun Kim · Gary Lee

摘要

Recent studies in difficulty-controlled reading comprehension item generation have leveraged large language models (LLMs) to produce items by adjusting difficulty-related features. However, existing methods typically rely on a single-agent prompting approach, which often fails to consistently sat...

智能体与工具评测与基准OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

Behavior-Aware Item Modeling via Dynamic Procedural Solution Representations for Knowledge Tracing

Jun Seo · Sangwon Ryu · Heejin Do · Hyounghun Kim · Gary Lee

摘要

Knowledge Tracing (KT) aims to predict learners’ future performance from past interactions. While recent KT approaches have improved via learning item representations aligned with Knowledge Components, they overlook the procedural dynamics of problem solving. We propose Behavior-Aware Item Modeli...

可解释性推理对齐与偏好OpenReview 项目页

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