Update app.py

app.py

@@ -70,9 +70,11 @@ intro = """
 
 We developed our open-domain conversational chatbots via instruction-tuning. This approach has a lot of limitations. Even though we can make a chatbot that can answer questions about anything, forcing the model to produce good-quality responses is hard. And by good, we mean **factual** and **nontoxic**  text. This leads us to two of the most common problems with generative models used in conversational applications:
 
-🤥 Generative models can perpetuate the generation of pseudo-informative content, that is, false information that may appear truthful.
+- **Hallucinations:** This model can produce content that can be mistaken for truth but is, in fact, misleading or entirely false, i.e., hallucination.
 
-🤬 In certain types of tasks, generative models can produce harmful and discriminatory content inspired by historical stereotypes.
+- **Biases and Toxicity:** This model inherits the social and historical stereotypes from the data used to train it. Given these biases, the model can produce toxic content, i.e., harmful, offensive, or detrimental to individuals, groups, or communities.
+
+- **Repetition and Verbosity:** The model may get stuck on repetition loops (especially if the repetition penalty during generations is set to a meager value) or produce verbose responses unrelated to the prompt it was given.
 
 ## Intended Use