.

app.py

@@ -85,7 +85,6 @@ def predict_product(num1, num2):
                 finished_per_model[model_name] = True
                 if valid_input:
                     if len([item for item in predicted_annotations_per_model[model_name] if item[1] is not None]) < len(ground_truth_digits_reversed):
-                        print ('jjere')
                         predicted_annotations_per_model[model_name].insert(0, ('⠀', 'wrong'))
                 continue
                 
@@ -155,7 +154,8 @@ demo = gr.Interface(
     title='Predicting Multiplication with GPT-2: Implicit vs. Explicit CoT',
     description='This demo showcases GPT-2\'s ability to directly predict the product of two large numbers without intermediate steps, using our stepwise internalization method. Compare the performance of implicit CoT (our method), no CoT, and explicit CoT. Implicit CoT offers accuracy and speed, while explicit CoT provides detailed reasoning but is slower.',
     article="""
-    - [Paper: From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step](https://arxiv.org/pdf/2405.14838)
+    - [Paper 1: Implicit Chain of Thought Reasoning via Knowledge Distillation](https://arxiv.org/pdf/2311.01460)
+    - [Paper 2: From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step](https://arxiv.org/pdf/2405.14838)
     - [Code Repository](https://github.com/da03/Internalize_CoT_Step_by_Step)
     - [Tweet Announcement](https://twitter.com/yuntiandeng/status/1795854740879774036)
     """,