Title: Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction. Abstract: With the recent success and popularity of pre-trained language models (LMs) in natural language processing, there has been a rise in efforts to understand their inner workings. 
In line with such interest, we propose a novel method that assists us in investigating the extent to which pre-trained LMs capture the syntactic notion of constituency. 
Our method provides an effective way of extracting constituency trees from the pre-trained LMs without training. 
In addition, we report intriguing findings in the induced trees, including the fact that pre-trained LMs outperform other approaches in correctly demarcating adverb phrases in sentences.