diff --git a/docs/advanced_guide/prompt.md b/docs/advanced_guide/prompt.md
index b64aed60fe54..20da981169cc 100644
--- a/docs/advanced_guide/prompt.md
+++ b/docs/advanced_guide/prompt.md
@@ -35,6 +35,7 @@ Prompt API 提供了这类算法实现的基本模块，支持[PET](https://arxi
 * [实践教程](#实践教程)
     * [文本分类示例](#文本分类示例)
     * 其他任务示例（待更新）
+* [Reference](#Reference)
 
 ## 如何定义模板
 
@@ -434,39 +435,39 @@ data_args, training_args = parser.parse_args_into_dataclasses()
 
 ```python
 
-    import paddle
-    from paddle.metric import Accuracy
-
-    # 损失函数
-    criterion = paddle.nn.CrossEntropyLoss()
-
-    # 评估函数
-    def compute_metrics(eval_preds):
-        metric = Accuracy()
-        correct = metric.compute(paddle.to_tensor(eval_preds.predictions),
-                                 paddle.to_tensor(eval_preds.label_ids))
-        metric.update(correct)
-        acc = metric.accumulate()
-        return {"accuracy": acc}
-
-    # 初始化
-    trainer = PromptTrainer(model=prompt_model,
-                            tokenizer=tokenizer,
-                            args=training_args,
-                            criterion=criterion,
-                            train_dataset=data_ds,
-                            eval_dataset=data_ds,
-                            callbacks=None,
-                            compute_metrics=compute_metrics)
-
-    # 训练模型
-    if training_args.do_train:
-        train_result = trainer.train(resume_from_checkpoint=None)
-        metrics = train_result.metrics
-        trainer.save_model()
-        trainer.log_metrics("train", metrics)
-        trainer.save_metrics("train", metrics)
-        trainer.save_state()
+import paddle
+from paddle.metric import Accuracy
+
+# 损失函数
+criterion = paddle.nn.CrossEntropyLoss()
+
+# 评估函数
+def compute_metrics(eval_preds):
+    metric = Accuracy()
+    correct = metric.compute(paddle.to_tensor(eval_preds.predictions),
+                             paddle.to_tensor(eval_preds.label_ids))
+    metric.update(correct)
+    acc = metric.accumulate()
+    return {"accuracy": acc}
+
+# 初始化
+trainer = PromptTrainer(model=prompt_model,
+                        tokenizer=tokenizer,
+                        args=training_args,
+                        criterion=criterion,
+                        train_dataset=data_ds,
+                        eval_dataset=data_ds,
+                        callbacks=None,
+                        compute_metrics=compute_metrics)
+
+# 训练模型
+if training_args.do_train:
+    train_result = trainer.train(resume_from_checkpoint=None)
+    metrics = train_result.metrics
+    trainer.save_model()
+    trainer.log_metrics("train", metrics)
+    trainer.save_metrics("train", metrics)
+    trainer.save_state()
 ```
 
 ## 实践教程
@@ -481,6 +482,14 @@ data_args, training_args = parser.parse_args_into_dataclasses()
 - [多层次文本分类示例](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/applications/text_classification/hierarchical/few-shot)
 
 
+## Reference
+
+- Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference. [[PDF]](https://arxiv.org/abs/2001.07676)
+- GPT Understands, Too. [[PDF]](https://arxiv.org/abs/2103.10385)
+- WARP: Word-level Adversarial ReProgramming. [[PDF]](https://aclanthology.org/2021.acl-long.381/)
+- RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning. [[PDF]](https://aclanthology.org/2022.findings-naacl.81/)
+- R-Drop: Regularized Dropout for Neural Networks. [[PDF]](https://arxiv.org/abs/2106.14448)
+
 ### 附录