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cs.AI2025

TORSO: Template-Oriented Reasoning Towards General Tasks

Minhyuk Kim, Seungyoon Lee, Heuiseok Lim

The approaches that guide Large Language Models (LLMs) to emulate human reasoning during response generation have emerged as an effective method for enabling them to solve complex…

cs.IR2025

Improving Korean-English Cross-Lingual Retrieval: A Data-Centric Study of Language Composition and Model Merging

Youngjoon Jang, Junyoung Son, Taemin Lee +5

With the increasing utilization of multilingual text information, Cross-Lingual Information Retrieval (CLIR) has become a crucial research area. However, the impact of training dat…

cs.CL2025

Semantic Aware Linear Transfer by Recycling Pre-trained Language Models for Cross-lingual Transfer

Seungyoon Lee, Seongtae Hong, Hyeonseok Moon +1

Large Language Models (LLMs) increasingly incorporate multilingual capabilities, fueling the demand to transfer them into target language-specific models. However, most approaches,…

cs.CL2025

FLEX: A Benchmark for Evaluating Robustness of Fairness in Large Language Models

Dahyun Jung, Seungyoon Lee, Hyeonseok Moon +2

Recent advancements in Large Language Models (LLMs) have significantly enhanced interactions between users and models. These advancements concurrently underscore the need for rigor…

cs.AI2025

Find the Intention of Instruction: Comprehensive Evaluation of Instruction Understanding for Large Language Models

Hyeonseok Moon, Jaehyung Seo, Seungyoon Lee +2

One of the key strengths of Large Language Models (LLMs) is their ability to interact with humans by generating appropriate responses to given instructions. This ability, known as…