9 papers
Decompose-and-Refine: Structured Legal Question Answering with Parametric Retrieval
Jihyung lee, Hyounghun Kim, Gary Lee
Large language models (LLMs) have shown strong performance in the legal domain, demonstrating notable potential in Legal Question Answering (LQA). However, unlike general QA, LQA r…
A Multi-Agent Framework for Feature-Constrained Difficulty Control in Reading Comprehension Item Generation
Seonjeong Hwang, Jun Seo, Hyounghun Kim +1
Recent studies in difficulty-controlled reading comprehension item generation have leveraged large language models (LLMs) to produce items by adjusting difficulty-related features.…
Difficulty-Controllable Cloze Question Distractor Generation
Seokhoon Kang, Yejin Jeon, Seonjeong Hwang +1
Multiple-choice cloze questions are commonly used to assess linguistic proficiency and comprehension. However, generating high-quality distractors remains challenging, as existing…
Can LLMs Estimate Cognitive Complexity of Reading Comprehension Items?
Seonjeong Hwang, Hyounghun Kim, Gary Geunbae Lee
Estimating the cognitive complexity of reading comprehension (RC) items is crucial for assessing item difficulty before it is administered to learners. Unlike syntactic and semanti…
Why Do Multilingual Reasoning Gaps Emerge in Reasoning Language Models?
Deokhyung Kang, Seonjeong Hwang, Daehui Kim +2
Reasoning language models (RLMs) achieve strong performance on complex reasoning tasks, yet they still exhibit a multilingual reasoning gap, performing better in high-resource lang…
Mixture-of-Experts with Intermediate CTC Supervision for Accented Speech Recognition
Wonjun Lee, Hyounghun Kim, Gary Geunbae Lee
Accented speech remains a persistent challenge for automatic speech recognition (ASR), as most models are trained on data dominated by a few high-resource English varieties, leadin…