6 citations · 18 across the 9 of their papers we have counts for
9 papers
Social Bias Evaluation for Large Language Models Requires Prompt Variations
Rem Hida, Masahiro Kaneko, Naoaki Okazaki
Warning: This paper contains examples of stereotypes and biases. Large Language Models (LLMs) exhibit considerable social biases, and various studies have tried to evaluate and mit…
Sampling-based Pseudo-Likelihood for Membership Inference Attacks
Masahiro Kaneko, Youmi Ma, Yuki Wata +1
Large Language Models (LLMs) are trained on large-scale web data, which makes it difficult to grasp the contribution of each text. This poses the risk of leaking inappropriate data…
A Little Leak Will Sink a Great Ship: Survey of Transparency for Large Language Models from Start to Finish
Masahiro Kaneko, Timothy Baldwin
Large Language Models (LLMs) are trained on massive web-crawled corpora. This poses risks of leakage, including personal information, copyrighted texts, and benchmark datasets. Suc…
Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting
Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki +1
There exist both scalable tasks, like reading comprehension and fact-checking, where model performance improves with model size, and unscalable tasks, like arithmetic reasoning and…
Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction
Masahiro Kaneko, Naoaki Okazaki
In Grammatical Error Correction (GEC), it is crucial to ensure the user's comprehension of a reason for correction. Existing studies present tokens, examples, and hints as to the b…
The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimated
Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki
Pre-trained language models trained on large-scale data have learned serious levels of social biases. Consequently, various methods have been proposed to debias pre-trained models.…