Decoupling World Knowledge from Language Models
Imagine you have a vast library filled with books on various subjects. While each book provides detailed information, the books are not organized in a way that makes it easy for you to quickly find the information you need. Similarly, human knowledge is complex and structured, making it challenging for AI language models to accurately represent it. Traditional symbolic AI research has focused on developing machine-friendly formats for Knowledge Representations (KRs), such as Description Logics, Prolog, Semantic Networks, and others. However, these formats are not enough to fully capture the complexity of human knowledge.
The Roadmap to Large Knowledge Models
To address this challenge, the article proposes a roadmap for developing more advanced LKMs. The first step is to decouple world knowledge from language models. This means creating separate representations for different types of knowledge, such as ontological knowledge, conceptual relationships, and others. Next, we need to develop inference machines that can handle these diverse representations seamlessly. Finally, we must integrate these inference machines into a unified framework to create the Large Knowledge Model.
Challenges in Developing LKMs
While the roadmap outlines the key steps towards developing LKMs, there are several challenges that need to be addressed along the way:
- Handling Conceptual Relationships: Human knowledge is often represented using concepts and relationships between them. Developing a model that can accurately represent these complex relationships is crucial for creating an effective LKM.
- Inference Machines: Traditional symbolic AI research has focused on developing inference machines that can handle the logical structure of KRs, but these machines are not adequate for handling the diverse representations of human knowledge. New inference machines need to be developed that can handle multiple forms of representation.
- Integration of Inference Machines: Once we have developed separate inference machines for different types of knowledge, we need to integrate them into a unified framework. This will allow the LKM to seamlessly handle diverse representations of knowledge and provide accurate inferences.
- Scalability: As the amount of knowledge grows, so does the complexity of the LKM. Developing models that can scale to accommodate this growth while maintaining accuracy is a significant challenge.
- Evaluation Metrics: Developing appropriate evaluation metrics for LKMs is crucial to assess their performance accurately. We need to develop metrics that can capture the complexity and diversity of human knowledge.
In conclusion, developing Large Knowledge Models that can accurately represent the complexity of human knowledge is a challenging task. However, with a clear roadmap and a focus on addressing the key challenges, we can create more advanced models that can handle the diverse representations of knowledge and provide accurate inferences.
