8 papers
DUD: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language Models
Yixin Bu, Runze Xia, Guanyun Zou +3
Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics often fail to capture the mo…
Toward Culturally Aligned LLMs through Ontology-Guided Multi-Agent Reasoning
Wonduk Seo, Wonseok Choi, Junseo Koh +7
Large Language Models (LLMs) increasingly support culturally sensitive decision making, yet often exhibit misalignment due to skewed pretraining data and the absence of structured…
MA-SAPO: Multi-Agent Reasoning for Score-Aware Prompt Optimization
Wonduk Seo, Juhyeon Lee, Junseo Koh +6
Prompt optimization has become a practical way to improve the performance of Large Language Models (LLMs) without retraining. However, most existing frameworks treat evaluation as…
Automated Visualization Code Synthesis via Multi-Path Reasoning and Feedback-Driven Optimization
Wonduk Seo, Daye Kang, Hyunjin An +7
Large Language Models (LLMs) have become a cornerstone for automated visualization code generation, enabling users to create charts through natural language instructions. Despite i…
SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science
Wonduk Seo, Juhyeon Lee, Yanjun Shao +3
Large Language Models (LLMs) have enabled dynamic reasoning in automated data analytics, yet recent multi-agent systems remain limited by rigid, single-path workflows that restrict…
Better by Comparison: Retrieval-Augmented Contrastive Reasoning for Automatic Prompt Optimization
Juhyeon Lee, Wonduk Seo, Hyunjin An +2
Automatic prompt optimization has recently emerged as a strategy for improving the quality of prompts used in Large Language Models (LLMs), with the goal of generating more accurat…