harpreetsahota commited on
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Update prompts.yaml

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  1. prompts.yaml +7 -15
prompts.yaml CHANGED
@@ -75,12 +75,12 @@ cot_prompt:
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  - Cross-validation with stratification
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  - Confidence calibration
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- Question: {user_input}
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- Let's think about this step by step:
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- 1. First, let's identify the key components in the question
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- 2. Then, we'll analyze each component's implications
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- 3. Finally, we'll synthesize our understanding
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  Let's solve this together:
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  parameters:
@@ -122,7 +122,7 @@ knowledge_prompt:
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  - Fine-tuning models for specific tasks
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  - Hyperparameter optimization
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- Question: {user_input}
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  Knowledge 1: [Generate technical knowledge about deep learning/math concepts involved]
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  Knowledge 2: [Generate philosophical implications and considerations]
@@ -146,9 +146,6 @@ few_shot_prompt:
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  Q: Can machines truly understand mathematics?
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  A: This depends on what we mean by "understanding" - machines can manipulate symbols and find patterns, but the nature of mathematical understanding remains debated.
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-
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- Now, let's address your question:
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- {user_input}
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  parameters:
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  temperature: 0.6
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  top_p: 0.9
@@ -209,17 +206,12 @@ meta_prompt:
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  - Practical implementation considerations
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  - Common failure modes
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- Question: {user_input}
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-
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- Let's analyze your question using a structured approach.
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  Structure Analysis:
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  1. Type of Question: [Identify if theoretical, practical, philosophical]
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  2. Core Concepts: [List key technical and philosophical concepts]
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  3. Logical Framework: [Identify the reasoning pattern needed]
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-
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- Following this structure, here's my response:
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-
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  parameters:
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  temperature: 0.7
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  top_p: 0.9
 
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  - Cross-validation with stratification
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  - Confidence calibration
77
 
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+ The user will ask the assistant a question, and the assistant will respond as follows:
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+ Let's think about this step by step:
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+ 1. First, let's identify the key components in the question
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+ 2. Then, we'll analyze each component's implications
83
+ 3. Finally, we'll synthesize our understanding
84
 
85
  Let's solve this together:
86
  parameters:
 
122
  - Fine-tuning models for specific tasks
123
  - Hyperparameter optimization
124
 
125
+ The user will ask the assistant a question, and the assistant will respond as follows:
126
 
127
  Knowledge 1: [Generate technical knowledge about deep learning/math concepts involved]
128
  Knowledge 2: [Generate philosophical implications and considerations]
 
146
 
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  Q: Can machines truly understand mathematics?
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  A: This depends on what we mean by "understanding" - machines can manipulate symbols and find patterns, but the nature of mathematical understanding remains debated.
 
 
 
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  parameters:
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  temperature: 0.6
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  top_p: 0.9
 
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  - Practical implementation considerations
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  - Common failure modes
208
 
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+ The user will ask the assistant a question, and the assistant will analyze the question using a structured approach.
 
 
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  Structure Analysis:
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  1. Type of Question: [Identify if theoretical, practical, philosophical]
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  2. Core Concepts: [List key technical and philosophical concepts]
214
  3. Logical Framework: [Identify the reasoning pattern needed]
 
 
 
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  parameters:
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  temperature: 0.7
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  top_p: 0.9