Study on comparative and discriminatory methodology of human and machine-generated languages

The discrimination of large language models (LLMs) against humans is an emerging issue in the real-world application of LLMs.The hallucination and collapse phenomenon is a crucial problem.The starting point for addressing these problems is to discriminate between human and machine texts.In this paper, we investigate the data construction, statistical analysis, including Latent Dirichlet Allocation (LDA), for the features, and the construction of the classifier.Through this process, we validate the possibility of certain features that are useful in discriminating the source of LLMs.We can construct a more elaborate classifier for discriminating between humans and LLMs, and furthermore, various LLMs.It can contribute to classifying the various LLMs and detecting their usage and their behaviors.