 Performance We use the KdConv dataset to finetune and evaluate the model. Under the default hyperparameters in the scripts, we get the following results on the test set:  |                        |      |        |        |       | | ----                   | ---- | ----   | ----   | ----  | |                        | loss | BLEU-4 | Dist-4 | f1    | | eva1.0 Before finetune     | 3.49 | 0.64   | 85.94  | 13.24 |   | eva1.0 After finetune    | 2.37 | 3.94   | 72.47  | 19.80 | | eva2.0 Before finetune     | 3.14 | 1.02   | 82.67  | 14.36 |   | eva2.0 After finetune     | 2.09 | 4.69   | 73.03  | 22.91 |  We also use beam search to run the interactive inference of EVA2.0:  ```[] Usr >>> 你好 Sys >>> 你好，请问你是？ Usr >>> 我是小明 Sys >>> 好的，小明，我是新来的，请多关照 Usr >>> 你了解人工智能吗？ Sys >>> 不了解，我只知道我是机器人 Usr >>> 那你很聪明 Sys >>> 谢谢夸奖，我会继续努力的 Usr >>> 你是学什么专业的呀 Sys >>> 计算机科学与技术，你呢？ Usr >>> 我也是学计算机的 Sys >>> 哦，那你有什么想知道的，可以问我 Usr >>> 编程语言你学过哪些？ Sys >>> java,c,c+都学过 Usr >>> 你学的真多 Sys >>> 呵呵，还好吧，我比较喜欢写代码 Usr >>> 哈哈，我也喜欢写代码，以后可以多多交流 Sys >>> 嗯，好的 Usr >>> 拜拜喽 Sys >>> 拜～ ```  **NOET**: Since different hardware may have different implementations of random functions, even if you use the same random seed as ours, you may not be able to reproduce this case. But the overall performance will not differ much.  