Update README.md

README.md

@@ -116,6 +116,8 @@ Train data should be a json file, where each line is a dict like this:
 `query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts. If you have no negative
 texts for a query, you can random sample some from the entire corpus as the negatives.
 
+Besides, for each query in the train data, we used LLMs to generate hard negative for them by asking LLMs to create a document that is the opposite one of the documents in 'pos'.
+
 ## Performance
 
 Below is a comparision table of the results we achieved compared to some other pre-trained Cross-Encoders on