arXiv subjects

Suho Kang

Publications and source records attributed to Suho Kang.

4 recordsLinked to original sources

arXiv2025

Tracing Mathematical Proficiency Through Problem-Solving Processes

Knowledge Tracing (KT) aims to model student's knowledge state and predict future performance to enable personalized learning in Intelligent Tutoring Systems. However, traditional KT methods face fundamental limitations in explainability, as they rely solely on the response correctness, neglecting the rich information embedded in students' problem-solving processes. To address this gap, we propose Knowledge Tracing Leveraging Problem-Solving Process (KT-PSP), which incorporates students' problem-solving processes to capture the multidimensional aspects of mathematical proficiency. We also introduce KT-PSP-25, a new dataset specifically designed for KT-PSP. Building on this, we present StatusKT, a KT framework that employs a teacher-student-teacher three-stage LLM pipeline to extract students' Mathematical Proficiency (MP) as intermediate representation. In this pipeline, the teacher LLM first extracts problem-specific proficiency indicators, then a student LLM generates responses based on the student's solution process, and a teacher LLM evaluates these responses to determine mastery of each indicator. The experimental results on KT-PSP-25 demonstrate that StatusKT improves the prediction performance of existing KT methods. Moreover, StatusKT provides interpretable explanations for its predictions by explicitly modeling students' mathematical proficiency. Code is available \href{https://github.com/jungyangpark/KT-PSP-25}{here}.

cs.LG↗

arXiv2025

A Black-Box Approach for Exogenous Replenishment in Online Resource Allocation

In a typical online resource allocation problem, we start with a fixed inventory of resources and make online allocation decisions in response to resource requests that arrive sequentially over a finite horizon. We consider settings where the inventory is replenished over time according to an unknown exogenous process. We introduce black-box methods that extend any existing algorithm, originally designed without considering replenishment, into one that works with an arbitrary (adversarial or stochastic) replenishment process. Our approach preserves the original algorithm's competitive ratio in regimes with large initial inventory, thereby enabling the seamless integration of exogenous replenishment into a large body of existing algorithmic results for both adversarial and stochastic arrival models.

cs.DS↗

arXiv2024

Benchmarking Foundation Models on Exceptional Cases: Dataset Creation and Validation

Foundation models (FMs) have achieved significant success across various tasks, leading to research on benchmarks for reasoning abilities. However, there is a lack of studies on FMs performance in exceptional scenarios, which we define as out-of-distribution (OOD) reasoning tasks. This paper is the first to address these cases, developing a novel dataset for evaluation of FMs across multiple modalities, including graphic novels, calligraphy, news articles, and lyrics. It includes tasks for instance classification, character recognition, token prediction, and text generation. The paper also proposes prompt engineering techniques like Chain-of-Thought (CoT) and CoT+Few-Shot to enhance performance. Validation of FMs using various methods revealed improvements. The code repository is accessible at: https://github.com/MLAI-Yonsei/ExceptionalBenchmark

cs.AI↗

arXiv2023

EPLKG: Efficient Prompt Learning with Knowledge Graph

Large-scale pre-trained models such as CLIP excel in transferability and robust generalization across diverse datasets. However, adapting these models to new datasets or domains is computationally costly, especially in low-resource or few-shot settings, and existing prompt-learning methods often lack interpretability. We introduce Efficient Prompt Learning with Knowledge Graph (EPLKG), which uses a knowledge graph to curate diverse, interpretable prompts and, where KG coverage is limited, augments this bank with LLM-generated human-readable visual descriptions. EPLKG operates entirely on cached CLIP image and text embeddings and employs a lightweight Gumbel-Softmax module to select a single prompt per image-class pair, enabling low-memory, fast training. Across 11 benchmarks, EPLKG reduces per-image training time by up to 45 percent and peak GPU memory by around 30 to 40 percent compared to strong prompt-learning baselines, while keeping the average base-new harmonic-mean accuracy within 2 percentage points, thereby improving the efficiency of model adaptation without sacrificing competitive performance or interpretability.

cs.AI↗