2 citations · 2 across the 10 of their papers we have counts for
10 papers
Localizing Input Uncertainty Quantification for Large Language Models via Shapley Values
Seongjun Lee, Suwan Yoon, Changhee Lee
As large language models (LLMs) are increasingly integrated into high-stakes decision-making, the ability to reliably quantify uncertainty has become a critical requirement for saf…
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…
DQE-CIR: Distinctive Query Embeddings through Learnable Attribute Weights and Target Relative Negative Sampling in Composed Image Retrieval
Geon Park, Ji-Hoon Park, Seong-Whan Lee
Composed image retrieval (CIR) addresses the task of retrieving a target image by jointly interpreting a reference image and a modification text that specifies the intended change.…
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…
KiC: Keyword-inspired Cascade for Cost-Efficient Text Generation with LLMs
Woo-Chan Kim, Ji-Hoon Park, Seong-Whan Lee
Large language models (LLMs) have demonstrated state-of-the-art performance across a wide range of natural language processing tasks. However, high-performing models are typically…
GRAIL: Gradient-Based Adaptive Unlearning for Privacy and Copyright in LLMs
Kun-Woo Kim, Ji-Hoon Park, Ju-Min Han +1
Large Language Models (LLMs) trained on extensive datasets often learn sensitive information, which raises significant social and legal concerns under principles such as the "Right…
SUGAR: Leveraging Contextual Confidence for Smarter Retrieval
Hanna Zubkova, Ji-Hoon Park, Seong-Whan Lee
Bearing in mind the limited parametric knowledge of Large Language Models (LLMs), retrieval-augmented generation (RAG) which supplies them with the relevant external knowledge has…