22 citations · 24 across the 8 of their papers we have counts for
8 papers
Citation-Closure Retrieval and Per-Rule Attribution for Real-World Regulatory Compliance Question Answering
Yeong-Joon Ju, Seong-Whan Lee
Deploying Large Language Models (LLMs) for regulatory compliance demands rigorous traceability via comprehensive citations across multi-tiered authority structures. Unlike traditio…
Multi-stage Prompt Refinement for Mitigating Hallucinations in Large Language Models
Jung-Woo Shim, Yeong-Joon Ju, Ji-Hoon Park +1
Recent advancements in large language models (LLMs) have shown strong performance in natural language understanding and generation tasks. However, LLMs continue to encounter challe…
CPR: Mitigating Large Language Model Hallucinations with Curative Prompt Refinement
Jung-Woo Shim, Yeong-Joon Ju, Ji-Hoon Park +1
Recent advancements in large language models (LLMs) highlight their fluency in generating responses to diverse prompts. However, these models sometimes generate plausible yet incor…
XLQA: A Benchmark for Locale-Aware Multilingual Open-Domain Question Answering
Keon-Woo Roh, Yeong-Joon Ju, Seong-Whan Lee
Large Language Models (LLMs) have shown significant progress in Open-domain question answering (ODQA), yet most evaluations focus on English and assume locale-invariant answers acr…
From Generator to Embedder: Harnessing Innate Abilities of Multimodal LLMs via Building Zero-Shot Discriminative Embedding Model
Yeong-Joon Ju, Seong-Whan Lee
Adapting generative Multimodal Large Language Models (MLLMs) into universal embedding models typically demands resource-intensive contrastive pre-training, while traditional hard n…
MIRe: Enhancing Multimodal Queries Representation via Fusion-Free Modality Interaction for Multimodal Retrieval
Yeong-Joon Ju, Ho-Joong Kim, Seong-Whan Lee
Recent multimodal retrieval methods have endowed text-based retrievers with multimodal capabilities by utilizing pre-training strategies for visual-text alignment. They often direc…
Explaining generative diffusion models via visual analysis for interpretable decision-making process
Ji-Hoon Park, Yeong-Joon Ju, Seong-Whan Lee
Diffusion models have demonstrated remarkable performance in generation tasks. Nevertheless, explaining the diffusion process remains challenging due to it being a sequence of deno…
NeuroInspect: Interpretable Neuron-based Debugging Framework through Class-conditional Visualizations
Yeong-Joon Ju, Ji-Hoon Park, Seong-Whan Lee
Despite deep learning (DL) has achieved remarkable progress in various domains, the DL models are still prone to making mistakes. This issue necessitates effective debugging tools…