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Sangkeun Jung

7 accepted papers

2025

AMACE: Automatic Multi-Agent Chart Evolution for Iteratively Tailored Chart Generation

EMNLP 2025

Many statistical facts are conveyed through charts. While various methods have emerged for chart understanding, chart generation typically requires users to manually input code, intent, and other parameters to obtain the desired format on chart generation tools. Recently, the advent of image-generat

Cited by 0SourcePDFScholar
2025

Courtroom-LLM: A Legal-Inspired Multi-LLM Framework for Resolving Ambiguous Text Classifications

COLING 2025main

In this research, we introduce the Courtroom-LLM framework, a novel multi-LLM structure inspired by legal courtroom processes, aiming to enhance decision-making in ambiguous text classification scenarios. Our approach simulates a courtroom setting within LLMs, assigning roles similar to those of pro…

Cited by 0SourcePDFScholar
2025

FEAT: A Preference Feedback Dataset through a Cost-Effective Auto-Generation and Labeling Framework for English AI Tutoring

ACL 2025short

In English education tutoring, teacher feedback is essential for guiding students. Recently, AI-based tutoring systems have emerged to assist teachers; however, these systems require high-quality and large-scale teacher feedback data, which is both time-consuming and costly to generate manually. In…

Cited by 0SourcePDFScholar
2025

ZEBRA: Leveraging Model-Behavioral Knowledge for Zero-Annotation Preference Dataset Construction

EMNLP 2025

Recent efforts in LLM alignment have focused on constructing large-scale preference datasets via human or Artificial Intelligence(AI) annotators. However, such approaches rely on instance-wise supervision, incurring substantial annotation cost and limited interpretability. In this paper, we propose

2024

Exploring Domain Robust Lightweight Reward Models based on Router Mechanism

ACL 2024findings

Recent advancements in large language models have heavily relied on the large reward model from reinforcement learning from human feedback for fine-tuning. However, the use of a single reward model across various domains may not always be optimal, often requiring retraining from scratch when new dom…

Cited by 0SourcePDFScholar
2024

Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition

ACL 2024short

While the abundance of rich and vast datasets across numerous fields has facilitated the advancement of natural language processing, sectors in need of specialized data types continue to struggle with the challenge of finding quality data. Our study introduces a novel guidance data augmentation tech…

Cited by 1SourcePDFScholar
2023

Semantic Ambiguity Detection in Sentence Classification using Task-Specific Embeddings

ACL 2023industry

Ambiguity is a major obstacle to providing services based on sentence classification. However, because of the structural limitations of the service, there may not be sufficient contextual information to resolve the ambiguity. In this situation, we focus on ambiguity detection so that service design…

Cited by 0SourcePDFScholar