1 citations · 1 across the 8 of their papers we have counts for
8 papers
Position: No Retroactive Cure for Infringement during Training
Satoru Utsunomiya, Masaru Isonuma, Junichiro Mori +1
As generative AI faces intensifying legal challenges, the machine learning community has increasingly relied on post-hoc mitigation -- especially machine unlearning and inference-t…
UniDetox: Universal Detoxification of Large Language Models via Dataset Distillation
Huimin Lu, Masaru Isonuma, Junichiro Mori +1
We present UniDetox, a universally applicable method designed to mitigate toxicity across various large language models (LLMs). Previous detoxification methods are typically model-…
Towards Transfer Unlearning: Empirical Evidence of Cross-Domain Bias Mitigation
Huimin Lu, Masaru Isonuma, Junichiro Mori +1
Large language models (LLMs) often inherit biases from vast amounts of training corpora. Traditional debiasing methods, while effective to some extent, do not completely eliminate…
Differentiable Instruction Optimization for Cross-Task Generalization
Masaru Isonuma, Junichiro Mori, Ichiro Sakata
Instruction tuning has been attracting much attention to achieve generalization ability across a wide variety of tasks. Although various types of instructions have been manually cr…
SciReviewGen: A Large-scale Dataset for Automatic Literature Review Generation
Tetsu Kasanishi, Masaru Isonuma, Junichiro Mori +1
Automatic literature review generation is one of the most challenging tasks in natural language processing. Although large language models have tackled literature review generation…
Trustworthy Human Computation: A Survey
Hisashi Kashima, Satoshi Oyama, Hiromi Arai +1
Human computation is an approach to solving problems that prove difficult using AI only, and involves the cooperation of many humans. Because human computation requires close engag…
Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance
Masaru Isonuma, Junichiro Mori, Danushka Bollegala +1
This paper presents a novel unsupervised abstractive summarization method for opinionated texts. While the basic variational autoencoder-based models assume a unimodal Gaussian pri…
Predictive Process Model Monitoring using Recurrent Neural Networks
Johannes De Smedt, Jochen De Weerdt
The field of predictive process monitoring focuses on case-level models to predict a single specific outcome such as a particular objective, (remaining) time, or next activity/rema…