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Found 6 papers, 2 papers with code
A Statistical and Multi-Perspective Revisiting of the Membership Inference Attack in Large Language Models
The lack of data transparency in Large Language Models (LLMs) has highlighted the importance of Membership Inference Attack (MIA), which differentiates trained (member) and untrained (non-member) data.
LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs
This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs).
Analyzing Social Biases in Japanese Large Language Models
With the development of Large Language Models (LLMs), social biases in the LLMs have become a crucial issue.
A Multi-Perspective Analysis of Memorization in Large Language Models
One of those behaviors is memorization, in which LLMs can generate the same content used to train them.
An empirical analysis of existing systems and datasets toward general simple question answering
Our analysis, including shifting of training and test datasets and training on a union of the datasets, suggests that our progress in solving SimpleQuestions dataset does not indicate the success of more general simple question answering.
Analyzing Word Embedding Through Structural Equation Modeling
Many researchers have tried to predict the accuracies of extrinsic evaluation by using intrinsic evaluation to evaluate word embedding.
