21 citations · 25 across the 16 of their papers we have counts for
16 papers
Is Discrete Difficulty Sufficient? Leveraging Continuous Difficulty for Efficient Self-Consistency in LLMs
Sihyeong Yeom, Geon Park, Geunyeong Jeong +3
Self-Consistency (SC) is a decoding strategy that samples diverse reasoning paths and selects the most consistent answer, demonstrating strong performance on complex reasoning prob…
GRASP: GRanularity-Aware Search Policy for Agentic RAG
Varun Gandhi, Jaewook Lee, Shantanu Todmal +4
Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers.…
Beyond Attack Success Rate: Temporal Logit Observability for LLM Safety Failures
Junyoung Park, Sunghwan Park, Seongyong Ju +1
Attack Success Rate (ASR) evaluates each jailbreak with a single yes/no label at the end of generation, telling us whether a failure happened but not how it unfolded. Two attacks t…
Who Am I? History-Aware Profiles for Student Simulation in Tutoring Dialogues
Zhangqi Duan, Shuyan Huang, Alexander Scarlatos +3
A key part of developing large language model (LLM)-powered, automated tutoring tools is student simulation, i.e., using LLMs to role-play as students, which can facilitate tutor m…
Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues
Shuyan Huang, Alexander Scarlatos, Jaewook Lee +1
Recent advances in large language models (LLMs) have led to the development of AI-powered tutoring systems that provide interactive support via dialogue. To enable these tutoring s…
Interrogating Design Homogenization in Web Vibe Coding
Donghoon Shin, Alice Gao, Rock Yuren Pang +3
Generative AI is known for its tendency to homogenize, often reproducing dominant style conventions found in training data. However, it remains unclear how these homogenizing effec…
Letting Tutor Personas Speak Up for LLMs: Learning Steering Vectors from Dialogue via Preference Optimization
Jaewook Lee, Alexander Scarlatos, Simon Woodhead +1
With the emergence of large language models (LLMs) as a powerful class of generative artificial intelligence (AI), their use in tutoring has become increasingly prominent. Prior wo…
Simulated Students in Tutoring Dialogues: Substance or Illusion?
Alexander Scarlatos, Jaewook Lee, Simon Woodhead +1
Advances in large language models (LLMs) enable many new innovations in education. However, evaluating the effectiveness of new technology requires real students, which is time-con…
Interpretable Mnemonic Generation for Kanji Learning via Expectation-Maximization
Jaewook Lee, Alexander Scarlatos, Andrew Lan
Learning Japanese vocabulary is a challenge for learners from Roman alphabet backgrounds due to script differences. Japanese combines syllabaries like hiragana with kanji, which ar…
PhoniTale: Phonologically Grounded Mnemonic Generation for Typologically Distant Language Pairs
Sana Kang, Myeongseok Gwon, Su Young Kwon +4
Vocabulary acquisition poses a significant challenge for second-language (L2) learners, especially when learning typologically distant languages such as English and Korean, where p…
From Text to Visuals: Using LLMs to Generate Math Diagrams with Vector Graphics
Jaewook Lee, Jeongah Lee, Wanyong Feng +1
Advances in large language models (LLMs) offer new possibilities for enhancing math education by automating support for both teachers and students. While prior work has focused on…
Training LLM-based Tutors to Improve Student Learning Outcomes in Dialogues
Alexander Scarlatos, Naiming Liu, Jaewook Lee +2
Generative artificial intelligence (AI) has the potential to scale up personalized tutoring through large language models (LLMs). Recent AI tutors are adapted for the tutoring task…
Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank
Jaewook Lee, Hunter McNichols, Andrew Lan
In this paper, we study an under-explored area of language and vocabulary learning: keyword mnemonics, a technique for memorizing vocabulary through memorable associations with a t…
Can Large Language Models Replicate ITS Feedback on Open-Ended Math Questions?
Hunter McNichols, Jaewook Lee, Stephen Fancsali +2
Intelligent Tutoring Systems (ITSs) often contain an automated feedback component, which provides a predefined feedback message to students when they detect a predefined error. To…
Math Multiple Choice Question Generation via Human-Large Language Model Collaboration
Jaewook Lee, Digory Smith, Simon Woodhead +1
Multiple choice questions (MCQs) are a popular method for evaluating students' knowledge due to their efficiency in administration and grading. Crafting high-quality math MCQs is a…
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models
Wanyong Feng, Jaewook Lee, Hunter McNichols +5
Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable format in assessments and practices.…