Update prompts.yaml
Browse files- 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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Let's solve this together:
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parameters:
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@@ -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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Knowledge 1: [Generate technical knowledge about deep learning/math concepts involved]
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Knowledge 2: [Generate philosophical implications and considerations]
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@@ -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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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
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@@ -209,17 +206,12 @@ meta_prompt:
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- Practical implementation considerations
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- Common failure modes
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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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Following this structure, here's my response:
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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
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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
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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:
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- Fine-tuning models for specific tasks
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- Hyperparameter optimization
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+
The user will ask the assistant a question, and the assistant will respond as follows:
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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]
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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
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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]
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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
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