6 citations · 7 across the 10 of their papers we have counts for
4 papers · 2 filters
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…
Eagle: Ethical Dataset Given from Real Interactions
Masahiro Kaneko, Danushka Bollegala, Timothy Baldwin
Recent studies have demonstrated that large language models (LLMs) have ethical-related problems such as social biases, lack of moral reasoning, and generation of offensive content…
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…
The Gaps between Pre-train and Downstream Settings in Bias Evaluation and Debiasing
Masahiro Kaneko, Danushka Bollegala, Timothy Baldwin
The output tendencies of Pre-trained Language Models (PLM) vary markedly before and after Fine-Tuning (FT) due to the updates to the model parameters. These divergences in output t…