24 citations · 53 across the 27 of their papers we have counts for
28 papers
Do LLMs Exhibit Coherent Knowledge Structures in Mathematical Reasoning? A Perspective from Knowledge Space Theory
Peng Cui, Heejin Do, Mrinmaya Sachan
Human knowledge is inherently structured and interdependent: mastery of a concept requires prior mastery of its prerequisites, a principle formalized by Knowledge Space Theory (KST…
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…
Benchmarking and Enhancing Text-to-Image Models for Generating Visual Representations in Early Arithmetic Education
Junling Wang, Boqi Chen, Heejin Do +3
AI systems are increasingly used to support educational content creation, yet it remains unclear whether they can generate outputs that faithfully represent the pedagogical concept…
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…
What Defines Good Reasoning in LLMs? Dissecting Reasoning Steps with Multi-Aspect Evaluation
Heejin Do, Jaehui Hwang, Dongyoon Han +2
Evaluating large language models (LLMs) on final-answer correctness is the dominant paradigm. This approach, however, provides a coarse signal for model improvement and overlooks t…
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…
Revisiting Early Detection of Sexual Predators via Turn-level Optimization
Jinmyeong An, Sangwon Ryu, Heejin Do +3
Online grooming is a severe social threat where sexual predators gradually entrap child victims with subtle and gradual manipulation. Therefore, timely intervention for online groo…
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…
Multi-Facet Blending for Faceted Query-by-Example Retrieval
Heejin Do, Sangwon Ryu, Jonghwi Kim +1
With the growing demand to fit fine-grained user intents, faceted query-by-example (QBE), which retrieves similar documents conditioned on specific facets, has gained recent attent…
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…
Multi-Dimensional Optimization for Text Summarization via Reinforcement Learning
Sangwon Ryu, Heejin Do, Yunsu Kim +2
The evaluation of summary quality encompasses diverse dimensions such as consistency, coherence, relevance, and fluency. However, existing summarization methods often target a spec…