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20242026
most citedQuantifying Conversational Reliability of Large Language Models under Multi-Turn Interaction

1 citations · 1 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CL20261 cited

Quantifying Conversational Reliability of Large Language Models under Multi-Turn Interaction

Jiyoon Myung

Large Language Models (LLMs) are increasingly deployed in real-world applications where users engage in extended, mixed-topic conversations that depend on prior context. Yet, their…

cs.IR2026

ReFeed: Retrieval Feedback-Guided Dataset Construction for Style-Aware Query Rewriting

Jiyoon Myung, Jungki Son, Kyungro Lee +2

Retrieval systems often fail when user queries differ stylistically or semantically from the language used in domain documents. Query rewriting has been proposed to bridge this gap…

cs.HC2025

Decomate: Leveraging Generative Models for Co-Creative SVG Animation

Jihyeon Park, Jiyoon Myung, Seone Shin +2

Designers often encounter friction when animating static SVG graphics, especially when the visual structure does not match the desired level of motion detail. Existing tools typica…

cs.IR2025

HyST: LLM-Powered Hybrid Retrieval over Semi-Structured Tabular Data

Jiyoon Myung, Jihyeon Park, Joohyung Han

User queries in real-world recommendation systems often combine structured constraints (e.g., category, attributes) with unstructured preferences (e.g., product descriptions or rev…

cs.CL2024

Efficient Technical Term Translation: A Knowledge Distillation Approach for Parenthetical Terminology Translation

Jiyoon Myung, Jihyeon Park, Jungki Son +2

This paper addresses the challenge of accurately translating technical terms, which are crucial for clear communication in specialized fields. We introduce the Parenthetical Termin…

cs.CV2024

Inpaint Biases: A Pathway to Accurate and Unbiased Image Generation

Jiyoon Myung, Jihyeon Park

This paper examines the limitations of advanced text-to-image models in accurately rendering unconventional concepts which are scarcely represented or absent in their training data…