Multi-party conversation is a popular form in online group chatting. However, the interweaving of utterance threads complicates the understanding of the dialogues for participants. Many conversation disentanglement models have been proposed using transformer-based pre-trained language models (PrLMs). However, advanced transformer-based PrLMs have not been extensively studied. This paper investigates the effectiveness of five advanced PrLMs: BERT, XLNet, ELECTRA, RoBERTa, and ModernBERT. The experimental results show that ELECTRA and RoBERTa are two PrLMs with outstanding performance than other PrLMs for the conversation disentanglement task.