1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CL2026
Exclusive Unlearning
Mutsumi Sasaki, Kouta Nakayama, Yusuke Miyao +2
When introducing Large Language Models (LLMs) into industrial applications, such as healthcare and education, the risk of generating harmful content becomes a significant challenge…
cs.LG2024
What's New in My Data? Novelty Exploration via Contrastive Generation
Masaru Isonuma, Ivan Titov
Fine-tuning is widely used to adapt language models for specific goals, often leveraging real-world data such as patient records, customer-service interactions, or web content in l…
cs.CL2024★ 1 cited
Unlearning Traces the Influential Training Data of Language Models
Masaru Isonuma, Ivan Titov
Identifying the training datasets that influence a language model's outputs is essential for minimizing the generation of harmful content and enhancing its performance. Ideally, we…