Overview
Contemporary AI systems face a fundamental tension between the flexibility of neural approaches and the precision of symbolic knowledge. Large language models demonstrate impressive general capabilities but often lack reliability in specialized domains where factual precision is critical. Meanwhile, symbolic knowledge representations (like knowledge graphs and ontologies) offer structured, verifiable information but struggle with natural language understanding and contextual reasoning.
Our research in this pillar focuses on creating systems that seamlessly integrate the complementary strengths of neural and symbolic approaches. We aim to develop frameworks that ground neural models in authoritative knowledge sources while maintaining their flexibility and generality. This integration is particularly crucial for domains like healthcare, law, and scientific research, where factual precision can be as important as natural language understanding.
Key Research Challenges
Knowledge Grounding
Large language models often generate plausible-sounding but factually incorrect information, especially in specialized domains. How can we effectively ground these models in authoritative knowledge sources while preserving their generative capabilities?
Efficient Domain Adaptation
Fine-tuning large models for every specialized domain is computationally prohibitive and risks catastrophic forgetting of general capabilities. How can we develop lightweight adaptation approaches that efficiently specialize models for different domains?
Symbolic-Neural Reasoning
Neural and symbolic systems use fundamentally different reasoning approaches. Neural models excel at handling ambiguity and context but struggle with precise logical inference, while symbolic systems offer rigorous reasoning but lack flexibility. How can we create hybrid reasoning mechanisms that leverage the strengths of both?
Knowledge Currency
Domain knowledge evolves rapidly, particularly in fields like medicine and science. How can systems efficiently update their knowledge without requiring constant retraining or extensive manual curation?
Cross-Domain Integration
Real-world problems often span multiple domains of expertise. How can systems effectively combine knowledge from different domains while respecting the unique terminology and reasoning patterns of each?
Research Questions
Our work in this pillar explores several interrelated research questions:
- How can structured knowledge sources (ontologies, knowledge graphs) be integrated with neural retrieval and generation systems in ways that preserve the strengths of both approaches?
- What architectures and training methods might enable efficient domain adaptation without requiring full model retraining or compromising general capabilities?
- How can systems effectively resolve conflicts between general model knowledge and domain-specific expertise?
- What inference mechanisms could combine the contextual understanding of neural models with the precision of symbolic reasoners?
- How might systems maintain and update their domain knowledge in response to evolving information without extensive manual curation?
- What evaluation frameworks can effectively measure both factual precision and contextual understanding in hybrid systems?
- How can systems transfer knowledge and reasoning patterns across related domains without explicit supervision?
Broader Directions
Our research in this pillar encompasses several broader directions:
Knowledge Graph-Enhanced Retrieval and Generation
Developing frameworks that use structured knowledge to guide retrieval, validate neural model outputs, and enhance generated responses with authoritative information.
Parameter-Efficient Domain Specialization
Creating lightweight adaptation techniques that enable models to acquire domain expertise without full retraining, potentially allowing hierarchical specialization across related domains.
Hybrid Reasoning Frameworks
Building systems that can seamlessly transition between neural and symbolic reasoning modes depending on the nature of the task, combining the flexibility of neural approaches with the rigor of symbolic inference.
Automated Knowledge Extraction and Integration
Exploring methods to automatically identify, extract, and incorporate domain knowledge from authoritative sources, reducing the need for manual knowledge engineering.
Cross-Domain Knowledge Transfer
Investigating approaches for transferring knowledge and reasoning patterns across related domains, enabling more efficient adaptation to new areas.
By advancing research in these directions, we aim to create systems that combine the breadth of general AI with the depth of domain expertise, addressing critical applications where both contextual understanding and factual precision are essential.