18 citations · 18 across the 1 of their papers we have counts for
3 papers
cs.CL2026
Can LLMs Be Constrained to the Past? Improving Knowledge Cutoff through Recall-Based Prompting
Michiro Asai, Ailiang Lin, Yu Kishimoto +4
Prompted knowledge cutoff instructs a large language model (LLM) to act as if information beyond a specified cutoff date were unavailable. However, prior work mainly relies on dire…
cs.LG2025★ 18 cited
Revisiting Dynamic Graph Clustering via Matrix Factorization
Dongyuan Li, Satoshi Kosugi, Ying Zhang +3
Dynamic graph clustering aims to detect and track time-varying clusters in dynamic graphs, revealing the evolutionary mechanisms of complex real-world dynamic systems. Matrix facto…
cs.CL2024
DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation
Aru Maekawa, Satoshi Kosugi, Kotaro Funakoshi +1
Dataset distillation aims to compress a training dataset by creating a small number of informative synthetic samples such that neural networks trained on them perform as well as th…