My best guess is that this crowded spot in embedding space is a sort of wastebasket for tokens that show up in machine-readable files but aren’t useful to the model for some reason. Possibly, these are tokens that are common in the corpus used to create the tokenizer, but not in the WebText training corpus. The oddly-specific tokens related to Puzzle & Dragons, Nature Conservancy, and David’s Bridal webpages suggest that BPE may have been run on a sample of web text that happened to have those websites overrepresented, and GPT-2 is compensating for this by shoving all the tokens it doesn’t find useful in the same place.
Previous related exploration: https://www.lesswrong.com/posts/BMghmAxYxeSdAteDc/an-exploration-of-gpt-2-s-embedding-weights