Key Challenges in AI Development
The AI community is witnessing a surge in projects that embrace and extend permissively licensed AI models. Despite this growth, these projects face significant challenges:
- Direct Contribution Limitations: Contributions to LLMs often result in forks, forcing users to choose a “best-fit” model that isn’t easily Maintaining these forks is also resource-intensive for model creators.
- High Barrier to Entry: The process of forking, training, and refining models requires substantial AI/ML expertise, limiting the ability of non-experts to contribute their
- Lack of Community Governance: There is no standardized community governance or best practices for reviewing, curating, and distributing forked models.
InstructLab addresses these challenges by enabling community contributors to add new “skills” or “knowledge” to existing models without the need for extensive retraining. This model-agnostic technology allows for the composition of new skills into models, facilitating regular builds of open-source licensed models with minimal overhead.
How InstructLab Works
InstructLab’s innovative approach allows contributors to enhance models by adding specific skills or knowledge. This is achieved through a novel synthetic data-based alignment tuning method, as detailed in the paper “LAB: Large-Scale Alignment for ChatBots” by researchers from the MIT-IBM Watson AI Lab and IBM Research. The LAB methodology leverages a taxonomy-guided synthetic data generation process and a multi-phase tuning framework, significantly reducing the reliance on expensive human annotations and proprietary models. The unique features of Instructlab are:
Model-Agnostic Technology: InstructLab’s technology is designed to work with any model, providing the infrastructure needed to create regular builds without the need for complete retraining.
Community-Driven Contributions: The project encourages contributions from individuals with varying levels of expertise, making it easier for anyone to shape the future of generative AI.
Enhanced Model Capabilities: By adding new skills and knowledge, InstructLab enhances the capabilities of existing models, making them more versatile and powerful.
InstructLab operates by enabling community contributors to enhance existing large language models (LLMs) through the addition of new “skills” or “knowledge”. This is achieved using a novel synthetic data-based alignment tuning method, which leverages a taxonomy-guided synthetic data generation process and a multi-phase tuning framework. Contributors can add specific skills or knowledge to models by following structured prompts and guidelines, ensuring high-quality and diverse data generation. The model-agnostic technology of InstructLab allows for the seamless integration of these contributions into regular builds of open-source licensed models, significantly reducing the overhead and complexity typically associated with model updates. This approach aims to democratize the process of enhancing LLMs, making it accessible to individuals with varying levels of expertise and fostering a collaborative community- driven development environment.
Real-World Applications
At DMS, we are leveraging the capabilities of InstructLab to develop a cutting-edge LLM-powered assistant designed to extract decision insights from legacy codebases. This innovative tool aims to model and enhance decision-making processes by analyzing legacy code and identifying key decision points. By utilizing InstructLab’s synthetic data generation and fine-tuning methodologies, we can efficiently train our models to understand and interpret complex code structures and decision logic. This not only accelerates the development process but also ensures that our assistant is equipped with high-quality, diverse training data, enabling it to provide accurate and actionable insights. InstructLab’s model-agnostic and community-driven approach has been instrumental in helping us achieve our goals, demonstrating the practical and transformative applications of this open-source AI project in real-world scenarios.
Examples
Here are some example skills that can be added to Large Language Models, with the help of InstructLab. To start, a few handcrafted examples of these tasks can be created, and used to generate a larger synthetic dataset with InstructLab, to finefune the model.
- Financial Analysis and Report Generation : Enhancing the model’s capability to analyze financial documents, generate detailed financial reports, perform risk assessments, and provide investment insights or market trend analysis.
- Medical Diagnosis Assistance : Training the model to assist with medical diagnostics by evaluating symptoms, suggesting potential diagnoses, and providing information on treatment options. This requires building a dataset with medical case studies and diagnosis records.
- Legal Document Drafting and Review : Augmenting the model to draft and review legal documents, provide legal advice, and help with contract analysis. This involves training on legal texts, case laws, and contract templates.
Conclusion
InstructLab is revolutionizing the way we contribute to and enhance large language models. By lowering the barriers to entry and fostering a community-driven approach, InstructLab empowers individuals from all backgrounds to shape the future of generative AI. Join the InstructLab community today and be a part of this exciting journey towards more accessible and powerful AI models.
References
- InstructLAB – https://github.com/instructlab
- LAB: Large-Scale Alignment for ChatBots ( https://arxiv.org/abs/2403.01081 )