In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Q: Follow the prior works BIBREF6, BIBREF7, BIBREF9, we adopt the BLEU and the Micro Entity F1 to evaluate our model performance.  We provide human evaluation on our framework and the compared models.  We hire several human experts and ask them to judge the quality of the responses according to correctness, fluency, and humanlikeness on a scale from 1 to 5.

A: What were the evaluation metrics?
****
Q: Participants who were shown the definition were more likely to suggest to ban the tweet. In fact, participants in group one very rarely gave different answers to questions one and two (18 of 500 instances or 3.6%). This suggests that participants in that group aligned their own opinion with the definition.

A: How did the authors demonstrate that showing a hate speech definition caused annotators to partially align their own opinion with the definition?
****
Q: This version highly surpasses the previous state of the art while still having fewer parameters than previous work.

A:
Do they compare results against state-of-the-art language models?
****