Exploring reasoning strategies
LLMs excel at pattern recognition in data but struggle with the symbolic reasoning required for complex multi-step problems.
Implementing more advanced reasoning strategies would make our research assistant far more capable. Hybrid systems that combine neural pattern completion with deliberate symbolic manipulation can master skills including these:
- Multi-step deductive reasoning to draw conclusions from a chain of facts
- Mathematical reasoning like solving equations through a series of transformations
- Planning tactics to break down a problem into an optimized sequence of actions
By integrating tools together with explicit reasoning steps instead of pure pattern completion, our agent can tackle problems requiring abstraction and imagination, and can arrive at a complex understanding of the world enabling them to hold more meaningful conversations about complex concepts.
An illustration of augmenting LLMs through...
