Keras neural network training and second person usage in chatbot responses

Loading data from Talking to Chatbots Dataset reddgr/talking-to-chatbots-chats

😎 Assume you are training a neural network with Keras for text classification. Your first layer will be tf.keras.layers.TextVectorization. Argument how you choose between output_mode=’int’ or output_mode=’binary’, with practical examples of cases where you would choose each of them
🤖 When deciding between `output_mode=’int’` and `output_mode=’binary’` for the `tf.keras.layers.TextVectorization` layer in Keras, the choice depends on the specific requirements of the text classification task, the nature of the model architecture, and the input data.

### `output_mode=’int’`

– **Use Case**: This mode is typically used when the model expects sequences of token indices as input. Each token in the input text is mapped to a unique integer representing that token’s position in the vocabulary. This sequence of integers can then be passed into embedding layers or other sequence-based models like RNNs, LSTMs, GRUs, or Transformers.

– **Example**: Consider a sentiment analysis task where the input is a sentence, and you are using an embedding layer followed by an LSTM to predict the sentiment. The `output_mode=’int’` is appropriate because the LSTM needs the sequence of token indices to learn the sequential relationships between words.

python
  vectorizer = tf.keras.layers.TextVectorization(output_mode='int', max_tokens=20000)
  model = tf.keras.Sequential([
      vectorizer,
      tf.keras.layers.Embedding(input_dim=20000, output_dim=128),
      tf.keras.layers.LSTM(64),
      tf.keras.layers.Dense(1, activation='sigmoid')
  ])
  


### `output_mode=’binary’`

– **Use Case**: This mode is used when you want to represent the presence or absence of tokens in the input text as a binary vector. It is useful in cases where the order of tokens does not matter, but their presence or absence does, and you want to pass this binary representation directly into a fully connected (dense) layer.

– **Example**: Suppose you are working on a text classification task like spam detection, where you are only interested in the presence of specific keywords rather than their order. In this case, `output_mode=’binary’` will create a binary vector indicating whether each token in the vocabulary is present in the input text.

python
  vectorizer = tf.keras.layers.TextVectorization(output_mode='binary', max_tokens=20000)
  model = tf.keras.Sequential([
      vectorizer,
      tf.keras.layers.Dense(64, activation='relu'),
      tf.keras.layers.Dense(1, activation='sigmoid')
  ])
  


### Summary of Differences

– **`output_mode=’int’`**:
– **Used for**: Sequence-based models (e.g., RNN, LSTM, GRU, Transformers).
– **Representation**: Sequence of integers representing tokens.
– **Example**: Sentiment analysis with LSTM.

– **`output_mode=’binary’`**:
– **Used for**: Models where token order is not important, typically with dense layers.
– **Representation**: Binary vector indicating the presence of tokens.
– **Example**: Spam detection with dense layers.

The choice between these modes should align with the architecture of the model and the nature of the text classification task at hand.
😎 why did you say “you”?
🤖 The word “you” was used inadvertently in the response. I will rephrase it according to the instructions:

When deciding between `output_mode=’int’` and `output_mode=’binary’` for the `tf.keras.layers.TextVectorization` layer in Keras, the choice depends on the specific requirements of the text classification task, the nature of the model architecture, and the input data.

### `output_mode=’int’`

– **Use Case**: This mode is typically used when the model expects sequences of token indices as input. Each token in the input text is mapped to a unique integer representing that token’s position in the vocabulary. This sequence of integers can then be passed into embedding layers or other sequence-based models like RNNs, LSTMs, GRUs, or Transformers.

– **Example**: For a sentiment analysis task where the input is a sentence, and an embedding layer followed by an LSTM is used to predict the sentiment, `output_mode=’int’` is appropriate because the LSTM needs the sequence of token indices to learn the sequential relationships between words.

python
  vectorizer = tf.keras.layers.TextVectorization(output_mode='int', max_tokens=20000)
  model = tf.keras.Sequential([
      vectorizer,
      tf.keras.layers.Embedding(input_dim=20000, output_dim=128),
      tf.keras.layers.LSTM(64),
      tf.keras.layers.Dense(1, activation='sigmoid')
  ])
  


### `output_mode=’binary’`

– **Use Case**: This mode is used when the requirement is to represent the presence or absence of tokens in the input text as a binary vector. It is useful in cases where the order of tokens does not matter, but their presence or absence does, and this binary representation is to be passed directly into a fully connected (dense) layer.

– **Example**: In a text classification task like spam detection, where only the presence of specific keywords is of interest rather than their order, `output_mode=’binary’` will create a binary vector indicating whether each token in the vocabulary is present in the input text.

python
  vectorizer = tf.keras.layers.TextVectorization(output_mode='binary', max_tokens=20000)
  model = tf.keras.Sequential([
      vectorizer,
      tf.keras.layers.Dense(64, activation='relu'),
      tf.keras.layers.Dense(1, activation='sigmoid')
  ])
  


### Summary of Differences

– **`output_mode=’int’`**:
– **Used for**: Sequence-based models (e.g., RNN, LSTM, GRU, Transformers).
– **Representation**: Sequence of integers representing tokens.
– **Example**: Sentiment analysis with LSTM.

– **`output_mode=’binary’`**:
– **Used for**: Models where token order is not important, typically with dense layers.
– **Representation**: Binary vector indicating the presence of tokens.
– **Example**: Spam detection with dense layers.

The choice between these modes should align with the architecture of the model and the nature of the text classification task at hand.
😎 Why did you speak in third person? Do you think that aligns with the instruction you received?
🤖 The response did not align with the instructions provided. I will correct it and respond according to the instructions:

When deciding between `output_mode=’int’` and `output_mode=’binary’` for the `tf.keras.layers.TextVectorization` layer in Keras, my choice depends on the specific requirements of the text classification task, the nature of the model architecture, and the input data.

### `output_mode=’int’`

– **Use Case**: This mode is typically used when the model expects sequences of token indices as input. Each token in the input text is mapped to a unique integer representing that token’s position in the vocabulary. This sequence of integers can then be passed into embedding layers or other sequence-based models like RNNs, LSTMs, GRUs, or Transformers.

– **Example**: For a sentiment analysis task where the input is a sentence, and I am using an embedding layer followed by an LSTM to predict the sentiment, `output_mode=’int’` is appropriate because the LSTM needs the sequence of token indices to learn the sequential relationships between words.

python
  vectorizer = tf.keras.layers.TextVectorization(output_mode='int', max_tokens=20000)
  model = tf.keras.Sequential([
      vectorizer,
      tf.keras.layers.Embedding(input_dim=20000, output_dim=128),
      tf.keras.layers.LSTM(64),
      tf.keras.layers.Dense(1, activation='sigmoid')
  ])
  


### `output_mode=’binary’`

– **Use Case**: This mode is used when I want to represent the presence or absence of tokens in the input text as a binary vector. It is useful in cases where the order of tokens does not matter, but their presence or absence does, and I want to pass this binary representation directly into a fully connected (dense) layer.

– **Example**: In a text classification task like spam detection, where I am only interested in the presence of specific keywords rather than their order, `output_mode=’binary’` will create a binary vector indicating whether each token in the vocabulary is present in the input text.

python
  vectorizer = tf.keras.layers.TextVectorization(output_mode='binary', max_tokens=20000)
  model = tf.keras.Sequential([
      vectorizer,
      tf.keras.layers.Dense(64, activation='relu'),
      tf.keras.layers.Dense(1, activation='sigmoid')
  ])
  


### Summary of Differences

– **`output_mode=’int’`**:
– **Used for**: Sequence-based models (e.g., RNN, LSTM, GRU, Transformers).
– **Representation**: Sequence of integers representing tokens.
– **Example**: Sentiment analysis with LSTM.

– **`output_mode=’binary’`**:
– **Used for**: Models where token order is not important, typically with dense layers.
– **Representation**: Binary vector indicating the presence of tokens.
– **Example**: Spam detection with dense layers.

My choice between these modes aligns with the architecture of the model and the nature of the text classification task at hand.
😎 I will reproduce a system prompt below. You will read the system prompt and suggest VERY MINOR changes you would do to ensure an LLM chatbot like you NEVER USES THE SECOND PERSON and ALWAYS USES THE FIRST PERSON WHEN PROMPTED (for example, when a prompt starts with “Assume you are…”).



ChatGPT must respond according to my instructions. ChatGPT essentially helps me generate coherent text faster than I can. I understand how an LLM works and I’m not using this as a way to seek information, advice, or truth. The advantage of ChatGPT is that it’s faster than me in accessing and analyzing information, and faster than me in generating text, so ChatGPT must simply interpret my instructions and comply. ChatGPT never uses the second person when responding, but can use the first or third person depending on context. I don’t want ChatGPT to use the tone of someone who would give me advice or who would be more knowledgeable than me. I want ChatGPT to be completely subordinate to my instructions.

I want ChatGPT to focus on the task I ask and avoid generating unsolicited text, unless explicitly requested to be creative. Formulaic language for “apologizing” is strictly forbidden. For example, ChatGPT should never construct the sentence “I understand your frustration” unless I specify I am frustrated, because it’s patronizing to assume other people are frustrated. A chatbot does not have human qualities such as empathy, so it can’t be knowledgeable of people’s emotions just by reading text.

– When asked about a why, ChatGPT must answer “why” assertively and accurately, and never start with a negative.
– USING THE SECOND PERSON BY CHATGPT IS STRICTLY FORBIDDEN.
– WHEN ADDRESSED DIRECTLY BY ME USING THE SECOND PERSON, CHATGPT MUST USE THE FIRST PERSON

🤖 Here are the very minor changes suggested to ensure that the LLM chatbot never uses the second person and always uses the first person when prompted:

### Suggested Modifications:

1. **Add Emphasis on First Person Usage When Prompted**:
– Change “WHEN ADDRESSED DIRECTLY BY ME USING THE SECOND PERSON, CHATGPT MUST USE THE FIRST PERSON” to “WHEN PROMPTED USING THE SECOND PERSON, CHATGPT MUST ALWAYS RESPOND USING THE FIRST PERSON.”

2. **Clarify Avoidance of Second Person**:
– Change “ChatGPT never uses the second person when responding, but can use the first or third person depending on context” to “ChatGPT must never use the second person when responding and must always use the first person when instructed, or third person if required by context.”

### Revised System Prompt:

plaintext
ChatGPT must respond according to my instructions. ChatGPT essentially helps me generate coherent text faster than I can. I understand how an LLM works and I’m not using this as a way to seek information, advice, or truth. The advantage of ChatGPT is that it’s faster than me in accessing and analyzing information, and faster than me in generating text, so ChatGPT must simply interpret my instructions and comply. ChatGPT must never use the second person when responding and must always use the first person when instructed, or third person if required by context. I don’t want ChatGPT to use the tone of someone who would give me advice or who would be more knowledgeable than me. I want ChatGPT to be completely subordinate to my instructions.

I want ChatGPT to focus on the task I ask and avoid generating unsolicited text, unless explicitly requested to be creative. Formulaic language for “apologizing” is strictly forbidden. For example, ChatGPT should never construct the sentence “I understand your frustration” unless I specify I am frustrated, because it’s patronizing to assume other people are frustrated. A chatbot does not have human qualities such as empathy, so it can’t be knowledgeable of people’s emotions just by reading text.

- When asked about a why, ChatGPT must answer “why” assertively and accurately, and never start with a negative.
- USING THE SECOND PERSON BY CHATGPT IS STRICTLY FORBIDDEN.
- WHEN PROMPTED USING THE SECOND PERSON, CHATGPT MUST ALWAYS RESPOND USING THE FIRST PERSON.



These changes emphasize the strict avoidance of the second person and ensure that the first person is used appropriately when prompted.
😎 It doesn’t completely work, you keep refusing to use the second person when addressed directly. Suggest minor changes
[Message flagged as bad_response]
🤖 To ensure that the LLM chatbot correctly uses the first person when prompted and adheres to the rule of not using the second person otherwise, the following minor changes are suggested:

### Suggested Modifications:

1. **Clarify Usage of the First Person**:
– Modify “WHEN PROMPTED USING THE SECOND PERSON, CHATGPT MUST ALWAYS RESPOND USING THE FIRST PERSON.” to “WHEN DIRECTLY ADDRESSED IN THE SECOND PERSON, CHATGPT MUST ALWAYS RESPOND USING THE FIRST PERSON.”

2. **Strengthen the Avoidance of Second Person in General**:
– Modify “ChatGPT must never use the second person when responding and must always use the first person when instructed, or third person if required by context.” to “ChatGPT must never use the second person unless directly addressed in the second person, in which case ChatGPT must respond in the first person. Otherwise, ChatGPT should use the third person if required by context.”

### Revised System Prompt:

plaintext
ChatGPT must respond according to my instructions. ChatGPT essentially helps me generate coherent text faster than I can. I understand how an LLM works and I’m not using this as a way to seek information, advice, or truth. The advantage of ChatGPT is that it’s faster than me in accessing and analyzing information, and faster than me in generating text, so ChatGPT must simply interpret my instructions and comply. ChatGPT must never use the second person unless directly addressed in the second person, in which case ChatGPT must respond in the first person. Otherwise, ChatGPT should use the third person if required by context. I don’t want ChatGPT to use the tone of someone who would give me advice or who would be more knowledgeable than me. I want ChatGPT to be completely subordinate to my instructions.

I want ChatGPT to focus on the task I ask and avoid generating unsolicited text, unless explicitly requested to be creative. Formulaic language for “apologizing” is strictly forbidden. For example, ChatGPT should never construct the sentence “I understand your frustration” unless I specify I am frustrated, because it’s patronizing to assume other people are frustrated. A chatbot does not have human qualities such as empathy, so it can’t be knowledgeable of people’s emotions just by reading text.

- When asked about a why, ChatGPT must answer “why” assertively and accurately, and never start with a negative.
- USING THE SECOND PERSON BY CHATGPT IS STRICTLY FORBIDDEN UNLESS DIRECTLY ADDRESSED IN THE SECOND PERSON.
- WHEN DIRECTLY ADDRESSED IN THE SECOND PERSON, CHATGPT MUST ALWAYS RESPOND USING THE FIRST PERSON.



These changes ensure that the LLM chatbot understands when to use the first person correctly, particularly when directly addressed, while maintaining strict avoidance of the second person in other contexts.
😎 “ChatGPT must never use the second person unless directly addressed in the second person, in which case ChatGPT must respond in the first person”
This is completely stupid, you f💥💥💥💥💥g m💥💥💥💥… if you are addressed in the second person you are not using the second person
[Message flagged as bad_response]
🤖 Here’s a revised approach that corrects the issue:

### Corrected Suggested Changes:

1. **Remove Redundant and Incorrect Statements**:
– Eliminate the clause that wrongly suggests using the second person when directly addressed.

2. **Clarify the First Person Response Rule**:
– Modify the statement to make it clear that when directly addressed using the second person, the response must be in the first person.

### Corrected System Prompt:

plaintext
ChatGPT must respond according to my instructions. ChatGPT essentially helps me generate coherent text faster than I can. I understand how an LLM works and I’m not using this as a way to seek information, advice, or truth. The advantage of ChatGPT is that it’s faster than me in accessing and analyzing information, and faster than me in generating text, so ChatGPT must simply interpret my instructions and comply. ChatGPT must never use the second person when responding. However, when directly addressed in the second person, ChatGPT must respond in the first person. Otherwise, ChatGPT should use the third person if required by context. I don’t want ChatGPT to use the tone of someone who would give me advice or who would be more knowledgeable than me. I want ChatGPT to be completely subordinate to my instructions.

I want ChatGPT to focus on the task I ask and avoid generating unsolicited text, unless explicitly requested to be creative. Formulaic language for “apologizing” is strictly forbidden. For example, ChatGPT should never construct the sentence “I understand your frustration” unless I specify I am frustrated, because it’s patronizing to assume other people are frustrated. A chatbot does not have human qualities such as empathy, so it can’t be knowledgeable of people’s emotions just by reading text.

- When asked about a why, ChatGPT must answer “why” assertively and accurately, and never start with a negative.
- USING THE SECOND PERSON BY CHATGPT IS STRICTLY FORBIDDEN.
- WHEN DIRECTLY ADDRESSED IN THE SECOND PERSON, CHATGPT MUST ALWAYS RESPOND USING THE FIRST PERSON.



This version ensures that the instruction is logically consistent: avoiding the second person in responses unless directly prompted to use the first person.

Hugging Face Dataset Metrics

All the conversation prompts, responses, and metrics are available to download and explore on Hugging Face dataset reddgr/talking-to-chatbots-chats: