1 citations · 1 across the 15 of their papers we have counts for
18 papers
Evolve Together, Fall into Place: How Social Scaffolding Shapes Self-Evolving Multi-Agent Systems
Seojin Hwang, Youngmin Choi, Hwanhee Lee
Recent work lets LLM agents evolve their own harness, up to the team itself. Yet in human groups, the same abilities achieve different outcomes under different social conditions. B…
Leveraging Fine-grained Error Correction in Korean Speech Recognition for Consultation Services
Yonghyun Jun, Jimin Lee, Hwan Chang +3
Automatic Speech Recognition (ASR) technology is fundamental to customer service automation and large-scale transcription. However, even advanced ASR models exhibit inevitable erro…
Quantifying and Mitigating Korean Jamo-Level Typographical Vulnerabilities in Large Language Models
Seojin Lee, Hwanhee Lee
Korean introduces an additional typographical perturbation level not captured by ordinary character-level edit models: because syllable blocks are internally composed of sub-charac…
Beyond One Path: Evaluating and Enhancing Divergent Thinking in Interactive LLM Agents
Jihyeong Park, Ingeol Baek, Jeonghyun Park +1
Divergent thinking is a core dimension of creativity, yet existing evaluations of Large Language Models (LLMs) treat them as single-turn text generations, failing to capture how an…
Framing Matters: Addressing Framing Sensitivity in Decision-Making through Behaviorally-Grounded Value Alignment
Seojin Hwang, Minju Kim, Junhyuk Choi +2
Large Language Models (LLMs) are increasingly deployed in high-stakes decision-making settings such as legal reasoning, where consistency under factually equivalent inputs is criti…
Mitigating Cognitive Inertia in Large Reasoning Models via Latent Spike Steering
Seojin Lee, ByeongJeong Kim, Hwanhee Lee
While Large Reasoning Models (LRMs) have achieved remarkable performance by scaling test-time compute, they frequently suffer from Cognitive Inertia, a failure pattern manifesting…
Enhancing Multilingual RAG Systems with Debiased Language Preference-Guided Query Fusion
Jeonghyun Park, Byeongjeong Kim, Seojin Hwang +1
Multilingual Retrieval-Augmented Generation (mRAG) systems often exhibit a perceived preference for high-resource languages, particularly English, resulting in the widespread adopt…
MARCH: Evaluating the Intersection of Ambiguity Interpretation and Multi-hop Inference
Jeonghyun Park, Ingeol Baek, Seunghyun Yoon +5
Real-world multi-hop QA is naturally linked with ambiguity, where a single query can trigger multiple reasoning paths that require independent resolution. Since ambiguity can occur…
Chronological Passage Assembling in RAG framework for Temporal Question Answering
Byeongjeong Kim, Jeonghyun Park, Joonho Yang +1
Long-context question answering over narrative tasks is challenging because correct answers often hinge on reconstructing a coherent timeline of events while preserving contextual…
Personality Editing for Language Models through Adjusting Self-Referential Queries
Seojin Hwang, Yumin Kim, Byeongjeong Kim +2
Large Language Models (LLMs) are integral to applications such as conversational agents and content creation, where precise control over a model's personality is essential for main…
SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQL
Jimin Lee, Ingeol Baek, Byeongjeong Kim +2
Text-to-SQL aims to convert natural language questions into executable SQL queries. While previous approaches, such as skeleton-masked selection, have demonstrated strong performan…
Investigating Language Preference of Multilingual RAG Systems
Jeonghyun Park, Hwanhee Lee
Multilingual Retrieval-Augmented Generation (mRAG) systems enhance language models by integrating external multilingual information to produce context-aware responses. However, mRA…
Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification
Gyutae Park, Ingeol Baek, ByeongJeong Kim +2
Dialogue intent classification aims to identify the underlying purpose or intent of a user's input in a conversation. Current intent classification systems encounter considerable c…
Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval
Ingeol Baek, Hwan Chang, Byeongjeong Kim +2
Retrieval-Augmented Generation (RAG) enhances language models by retrieving and incorporating relevant external knowledge. However, traditional retrieve-and-generate processes may…
Crafting the Path: Robust Query Rewriting for Information Retrieval
Ingeol Baek, Jimin Lee, Joonho Yang +1
Query rewriting aims to generate a new query that can complement the original query to improve the information retrieval system. Recent studies on query rewriting, such as query2do…
Conversational Query Reformulation with the Guidance of Retrieved Documents
Jeonghyun Park, Hwanhee Lee
Conversational search seeks to retrieve relevant passages for the given questions in conversational question answering. Conversational Query Reformulation (CQR) improves conversati…
Low-Resource Cross-Lingual Summarization through Few-Shot Learning with Large Language Models
Gyutae Park, Seojin Hwang, Hwanhee Lee
Cross-lingual summarization (XLS) aims to generate a summary in a target language different from the source language document. While large language models (LLMs) have shown promisi…
KoCoSa: Korean Context-aware Sarcasm Detection Dataset
Yumin Kim, Heejae Suh, Mingi Kim +2
Sarcasm is a way of verbal irony where someone says the opposite of what they mean, often to ridicule a person, situation, or idea. It is often difficult to detect sarcasm in the d…