.

app.py

@@ -58,11 +58,6 @@ def predict_product(num1, num2):
         ground_truth_annotations = ground_truth_annotations[::-1]
         # Predicted
         for model_name in models:
-            import time
-            if not finished_per_model['implicit']:
-                time.sleep(0.034)
-            else:
-                time.sleep(0.01)
             model = models[model_name]
             if finished_per_model[model_name]:
                 continue
@@ -146,6 +141,13 @@ demo = gr.Interface(
         gr.HighlightedText(label='No CoT Prediction', combine_adjacent=False, show_legend=False, color_map=color_map, show_inline_category=False),
         gr.HighlightedText(label='Explicit CoT Steps & Prediction', combine_adjacent=False, show_legend=False, color_map=color_map, show_inline_category=False),
     ],
+    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)
+    - [Code Repository](https://github.com/da03/Internalize_CoT_Step_by_Step)
+    - [Tweet Announcement](https://twitter.com/yuntiandeng/status/1795854740879774036)
+    """,
     clear_btn=None,
     submit_btn="Multiply!",
     live=False,