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cs.CL2026

Learning When to Translate for Multilingual Reasoning

Deokhyung Kang, Hyounghun Kim, Gary Geunbae Lee

Reasoning language models (RLMs) achieve strong performance on complex reasoning tasks, but still exhibit substantial multilingual reasoning gaps, largely due to language-understan…

cs.CL2026

Understanding LLM Behavior in Multi-Target Cross-Lingual Summarization

Sangwon Ryu, Yihong Liu, Mingyang Wang +4

Multi-target cross-lingual text summarization (MTXLS), which summarizes a source document into multiple target languages, is increasingly important as users consume content in dive…

cs.CL2025

Self-Correcting Code Generation Using Small Language Models

Jeonghun Cho, Deokhyung Kang, Hyounghun Kim +1

Self-correction has demonstrated potential in code generation by allowing language models to revise and improve their outputs through successive refinement. Recent studies have exp…

cs.CL2025

K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected Compressor

Jeonghun Cho, Gary Geunbae Lee

Retrieval-augmented question answering (QA) integrates external information and thereby increases the QA accuracy of reader models that lack domain knowledge. However, documents re…

cs.CL2025

Retrieval-Augmented Fine-Tuning With Preference Optimization For Visual Program Generation

Deokhyung Kang, Jeonghun Cho, Yejin Jeon +4

Visual programming languages (VPLs) allow users to create programs through graphical interfaces, which results in easier accessibility and their widespread usage in various domains…

cs.CL2025

EnSToM: Enhancing Dialogue Systems with Entropy-Scaled Steering Vectors for Topic Maintenance

Heejae Suh, Yejin Jeon, Deokhyung Kang +3

Small large language models (sLLMs) offer the advantage of being lightweight and efficient, which makes them suitable for resource-constrained environments. However, sLLMs often st…