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Jianzhe Chai · YU Zhe · Jun Sakuma
Benchmark-based evaluation is the de facto standard for comparing large language models (LLMs). However, its reliability is increasingly threatened by test set contamination, where test samples or their close variants leak into training data and artificially inflate reported performance. To addre...
Junki Mori · Kazuya Kakizaki · Taiki Miyagawa · Jun Sakuma
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding them in external knowledge. However, its application in sensitive domains is limited by privacy risks. Existing private RAG methods typically rely on query-time differential privacy (DP), which requires repeat...
Ragib Amin Nihal · Rui Wen · Kazuhiro Nakadai · Jun Sakuma
Large language models (LLMs) remain vulnerable to multi-turn jailbreaking attacks that exploit conversational context to bypass safety constraints gradually. These attacks target different harm categories through distinct conversational approaches. Existing multi-turn methods often rely on heuris...
Chia-Yi Hsu · Yu-Lin Tsai · Zhe Yu · Yan-Lun Chen · Chih-Hsun Lin · Chia-Mu Yu · Yang Zhang · Chun-Ying Huang · Jun Sakuma