Heejin Do

Last Translation Benchmark

Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263

For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…

SWIM: Student Writing Simulation via Proficiency-Conditioned Generation

Heejin Do, Jakub Kontak, Mrinmaya Sachan

Writing proficiency manifests in how students develop content, organize ideas, choose words, and use language. Despite growing interest in LLM-based student simulation, whether LLM…

GPAgentBench-2K: Benchmarking Large Language Model Agents in Complex Clinical Action Space

Boqi Chen, Xudong Liu, Yunke Ao +2

Large Language Models (LLMs) show great potential as clinical agents, yet existing benchmarks reduce clinical workflows to static predictions or unconstrained Markov Decision Proce…

NL2Scratch: An Executable Benchmark and Evaluation for Block-Based Programming

Heejin Do, Alexandre Ballenghien, Yang Wu +1

Block-based programming environments such as Scratch are widely used in early programming education, yet natural-language-to-code (NL2Code) research has focused primarily on text-b…

Segment-level Tree Search for Long Meeting Document Summarization

Sangwon Ryu, Heejin Do, Jun Seo +4

Meeting documents are challenging to summarize due to their length and complex conversational structure. Existing approaches typically adopt multi-stage pipelines that extract info…

Simulating Students or Sycophantic Problem Solving? On Misconception Faithfulness of LLM Simulators

Heejin Do, Shashank Sonkar, Mrinmaya Sachan

Large language models (LLMs) can fluently generate student-like responses, making them attractive as simulated students for training and evaluating AI tutors and human educators. Y…

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

Jun Seo, Sangwon Ryu, Heejin Do +2

Knowledge Tracing (KT) aims to predict learners' future performance from past interactions. While recent KT approaches have improved via learning item representations aligned with…

Leveraging What's Overfixed: Post-Correction via LLM Grammatical Error Overcorrection

Taehee Park, Heejin Do, Gary Geunbae Lee

Robust supervised fine-tuned small Language Models (sLMs) often show high reliability but tend to undercorrect. They achieve high precision at the cost of low recall. Conversely, L…

Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search

Sangwon Ryu, Heejin Do, Yunsu Kim +2

Controllable summarization moves beyond generic outputs toward human-aligned summaries guided by specified attributes. In practice, the interdependence among attributes makes it ch…

cs.CL2025★ 2 cited

Teach-to-Reason with Scoring: Self-Explainable Rationale-Driven Multi-Trait Essay Scoring

Heejin Do, Sangwon Ryu, Gary Geunbae Lee

Multi-trait automated essay scoring (AES) systems provide a fine-grained evaluation of an essay's diverse aspects. While they excel in scoring, prior systems fail to explain why sp…

Towards Prompt Generalization: Grammar-aware Cross-Prompt Automated Essay Scoring

Heejin Do, Taehee Park, Sangwon Ryu +1

In automated essay scoring (AES), recent efforts have shifted toward cross-prompt settings that score essays on unseen prompts for practical applicability. However, prior methods t…

Multimodal Cognitive Reframing Therapy via Multi-hop Psychotherapeutic Reasoning

Subin Kim, Hoonrae Kim, Heejin Do +1

Previous research has revealed the potential of large language models (LLMs) to support cognitive reframing therapy; however, their focus was primarily on text-based methods, often…

DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognition

Wonjun Lee, Solee Im, Heejin Do +3

Dysarthric speech recognition often suffers from performance degradation due to the intrinsic diversity of dysarthric severity and extrinsic disparity from normal speech. To bridge…

Exploring Iterative Controllable Summarization with Large Language Models

Sangwon Ryu, Heejin Do, Daehee Kim +5

Large language models (LLMs) have demonstrated remarkable performance in abstractive summarization tasks. However, their ability to precisely control summary attributes (e.g., leng…

Multi-aspect Depression Severity Assessment via Inductive Dialogue System

Chaebin Lee, Seungyeon Seo, Heejin Do +1

With the advancement of chatbots and the growing demand for automatic depression detection, identifying depression in patient conversations has gained more attention. However, prio…

Autoregressive Multi-trait Essay Scoring via Reinforcement Learning with Scoring-aware Multiple Rewards

Heejin Do, Sangwon Ryu, Gary Geunbae Lee

Recent advances in automated essay scoring (AES) have shifted towards evaluating multiple traits to provide enriched feedback. Like typical AES systems, multi-trait AES employs the…

Acoustic Feature Mixup for Balanced Multi-aspect Pronunciation Assessment

Heejin Do, Wonjun Lee, Gary Geunbae Lee

In automated pronunciation assessment, recent emphasis progressively lies on evaluating multiple aspects to provide enriched feedback. However, acquiring multi-aspect-score labeled…

Key-Element-Informed sLLM Tuning for Document Summarization

Sangwon Ryu, Heejin Do, Yunsu Kim +2

Remarkable advances in large language models (LLMs) have enabled high-quality text summarization. However, this capability is currently accessible only through LLMs of substantial…