2 citations · 2 across the 10 of their papers we have counts for
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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…
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…