Types of Training Datasets

• Instruction Tuning Datasets

  These datasets are designed to teach models how to follow specific instructions accurately. They work by pairing instructions with their ideal responses, creating a clear learning path for the model. Each entry consists of an instruction, optional input context, and the expected output. This structure helps models develop better task-following capabilities and more accurate responses.

  Example:

  • Instruction: “Analyze this research paper and provide a detailed summary focusing on the methodology and key findings.”
  • Input: “Title: Impact of Meditation on Cognitive Function in Older Adults…”
  • Output: “This longitudinal study investigated the effects of daily meditation…”

• Preference Datasets

  Preference datasets help models learn to distinguish better responses from less effective ones. By providing multiple responses to the same prompt and indicating which is preferred, these datasets teach models about response quality and appropriateness. Each example includes clear reasoning about why certain responses are better, helping models understand the nuances of good responses.

  Example:

  • Prompt: “Explain photosynthesis to a child.”

  • Response A: “It’s when plants eat sunlight to make food.”

  • Response B: “Plants use sunlight to make their own food, like having a tiny solar-powered kitchen.”

  • Preferred: Response B (More descriptive and uses a relatable analogy)

• Sentiment Analysis Datasets

  These datasets train models to recognize and interpret emotional tones in text. They include various text samples labeled with their emotional content, helping models understand the subtle differences in expression. The datasets often include different intensity levels and contextual nuances, enabling models to grasp the complexity of human emotions in text.

  Example:

  • Text: “I love this product; it’s amazing!”
  • Sentiment: Positive

LLM Instruction tuning Quick Start Datasets

• Get started with LLM instruction tuning datasets by accessing the CSV files here:

  • 🔗 Quick Start: CSV Datasets for LLM Instruction Tuning

• Get started with LLM Preference tuning datasets by accessing the CSV files here:

  • 🔗 Quick Start: CSV Datasets for LLM Prefernce Tuning