TheRise of Automated Instruction Tuning (Wizard ML)

The Rise of Automated Instruction Tuning: Decoding WizardLM and Evil Instruct

The landscape of large language models (LLMs) is shifting. We're moving from models trained on raw text to instruction-based models, capable of executing specific tasks as directed. However, achieving this level of responsiveness requires more than just vast text datasets. It necessitates fine-tuning on meticulously crafted instruction data, a process traditionally reliant on expensive and potentially limited human-generated examples.

This blog post delves into the innovative approach presented in the "WizardLM" paper, which introduces "Evil Instruct," a method for automatically generating complex and diverse instruction datasets, culminating in the creation of the powerful WizardLM model.

The Traditional Paradigm: From Base Models to Instruction-Tuned LLMs

The conventional workflow involves two primary stages:

Base Model Training: A foundation model, like GPT DaVinci or Llama, is trained on massive amounts of internet text. This equips the model with a broad knowledge base.
Instruction Fine-Tuning: The base model is further refined using instruction-response pairs, enabling it to follow specific commands. This process yields models like ChatGPT, which excel at responding to user instructions.

However, human-generated instruction datasets, while effective, are often:

Expensive: Acquiring human-annotated data is resource-intensive.
Limited in Diversity: Human-created examples may lack the complexity and variety needed for robust model training.

WizardLM and Evil Instruct: Automating Instruction Data Creation

WizardLM seeks to overcome these limitations by automating the creation of instruction datasets. The core concept revolves around "Evil Instruct," a method that leverages a base model (e.g., Llama) to generate diverse and challenging instructions.

Key Goals of Evil Instruct:

Automated Data Creation: Eliminate the reliance on human annotators.
Increased Complexity and Diversity: Generate instructions that surpass the difficulty and variety of human-created datasets.

The Evil Instruct Methodology:

Seed Dataset: The process begins with a seed dataset of instruction-response pairs, such as the Vicuna dataset.
Instruction Evolution: Instructions are evolved through two primary techniques:
- In-Depth Evolving: This method enhances existing instructions by adding constraints, deepening context, concretizing concepts, increasing reasoning steps, or complicating inputs.
- In-Breadth Evolving: This technique generates entirely new instructions within the same domain, promoting topic and skill diversity.
Instruction Filtering: A series of automated checks eliminates poorly generated instructions, ensuring data quality. These checks include:
- Removing duplicate instructions.
- Filtering out instructions that the model struggles to respond to.
- Eliminating responses that are nonsensical or simply copies of the prompt.
Model Training: The evolved instruction dataset is used to fine-tune the base model, resulting in WizardLM.

In-Depth Evolving in Detail:

This method uses prompt engineering to modify existing instructions. It uses five main techniques.

Add Constraints: Adding limitations or requirements to the instruction.
Deepening: Expanding the context of questions in the prompt.
Concretizing: Replacing general concepts with specific ones.
Increased Reasoning Steps: Requiring the model to show its reasoning process.
Complicated Input: Adding data formats (e.g., XML, HTML) to the instruction.

In-Breadth Evolving in Detail:

This method focuses on creating new instructions within the same topic domain, but that are more rare. It prompts the model to create a new instruction that is related, but also unique.

WizardLM's Performance:

WizardLM demonstrates strong performance, often surpassing human-trained models on various benchmarks. Notably, it excels in handling high-difficulty instructions, even outperforming ChatGPT in certain scenarios.

Strengths and Weaknesses:

Strengths:
- Effective in generating diverse and complex instructions.
- Reduces the cost and reliance on human annotators.
- Performs well on high-difficulty tasks.
- Excels in philosophy, technology, physics and ethics tasks.
Weaknesses:
- Struggles with reasoning, code generation, and debugging.
- Limited multilingual capabilities.
- Common sense and art tasks are also areas of weakness.

The Future of Automated Instruction Tuning:

WizardLM represents a significant step towards automated model training. The ability to generate high-quality instruction datasets without extensive human intervention opens up new possibilities for LLM development. As this technology evolves, we can expect to see even more sophisticated models capable of tackling complex tasks with greater efficiency.

The concept of models training models is an exciting one, and WizardLM provides a compelling glimpse into this future.

Links to follow for this blog post:

Wizard-LM

https://github.com/nlpxucan/WizardLM

https://tinyurl.com/ColabSerapi-API

https://tinyurl.com/ColabPinecone-API

https://github.com/yoheinakajima/babyagi

Mergekit GUI:

https://huggingface.co/spaces/arcee-ai/mergekit-gui

Mergekit Config Generator:

https://huggingface.co/spaces/arcee-ai/mergekit-config-generator

Arcee AI Mergekit GitHub: https://github.com/arcee-ai/mergekit