5 citations · 5 across the 4 of their papers we have counts for
4 papers
From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines
Sunkyung Lee, Jihye Back, Donghyeon Jeon +4
Generative information retrieval (GenIR) formulates the retrieval process as a text-to-text generation task, leveraging the vast knowledge of large language models. However, existi…
ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models
Junho Yoon, Geom Lee, Donghyeon Jeon +2
Quantization has been widely studied as an effective technique for reducing the memory requirement of large language models (LLMs), potentially improving the latency time as well.…
QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Engines
Ohjoon Kwon, Changsu Lee, Jihye Back +3
Large language models (LLMs) have been widely used for relevance assessment in information retrieval. However, our study demonstrates that combining two distinct small language mod…
SLM as Guardian: Pioneering AI Safety with Small Language Models
Ohjoon Kwon, Donghyeon Jeon, Nayoung Choi +6
Most prior safety research of large language models (LLMs) has focused on enhancing the alignment of LLMs to better suit the safety requirements of humans. However, internalizing s…