TLDR: Embodied-LM is a new neurosymbolic system that improves Large Language Models’ (LLMs) logical reasoning by grounding their understanding in ‘image schemas’ – fundamental cognitive patterns derived from sensorimotor experiences. The system uses LLMs to interpret natural language scenarios and translate them into formal programs for a symbolic solver, leveraging Declarative Spatial Reasoning (DSR) within Answer Set Programming (ASP). This approach allows LLMs to reason about spatial and other abstract concepts by mapping them to concrete spatial relationships, demonstrating enhanced interpretability and competitive performance on logical deduction and puzzle-solving tasks.
Researchers François Olivier and Zied Bouraoui have introduced a new neurosymbolic system called Embodied-LM, aiming to enhance the logical reasoning capabilities of Large Language Models (LLMs). Despite significant advancements in natural language understanding, LLMs often struggle with complex logical deduction, lacking the robust mental representations that allow humans to comprehend and reason about situations effectively.
The core idea behind Embodied-LM is to ground understanding and logical reasoning in what are known as image schemas. These are recurring patterns derived from our sensorimotor experiences, forming the fundamental structures of human cognition. Think of basic concepts like ‘container’ or ‘path’ – these aren’t just abstract ideas but are deeply rooted in our physical interactions with the world. The system operationalizes these spatial foundations using declarative spatial reasoning within Answer Set Programming (ASP).
Understanding Image Schemas
Cognitive research suggests that humans understand language by relying on a finite set of conceptual primitives, which they combine and use in mental simulations. These primitives, categorized into spatial, spatiotemporal, and force-dynamic, originate from early sensorimotor experiences. When combined, they form image schemas. For instance, the ‘OBJECT_INTO_CONTAINER’ schema, derived from physically putting objects into containers, helps us understand not only literal sentences like “Bill is in the house” but also more abstract concepts such as “Berlin is in Germany” or “to be in love.”
Embodied-LM provides a computational realization of this image schema-based reasoning. Its architecture leverages LLMs to identify appropriate schematic structures for given scenarios and then translates these into formal programs. These programs are processed by Clingo, an Answer Set Programming system, enhanced with the Declarative Spatial Reasoning (DSR) framework. While the full theoretical approach envisions incorporating all types of primitives, the current proof-of-concept focuses on foundational spatial primitives, demonstrating their effectiveness in reasoning tasks.
How Embodied-LM Works
The system is designed for scenarios involving a context, a question, and possible answer options. The LLM component, specifically GPT-4, generates ASP programs by interpreting natural language scenarios through image schemas. A system prompt guides the LLM on available spatial predicates (like ‘on_ps’ for a point on a segment, or ‘in_pr’ for a point inside a rectangle), their semantics, and syntactic requirements. This guidance ensures that the LLM consistently interprets concepts – for example, if “newer” is interpreted as “to the right” in one rule, it must maintain that interpretation throughout.
The Declarative Spatial Reasoning (DSR) framework is crucial here. It allows users to define qualitative spatial relations and reason about the constraints they imply. Geometric shapes like points, line segments, and rectangles are characterized as objects in DSR. Spatial relations, such as ‘inside’ or ‘left of’, are formalized through equations and inequalities involving these geometric parameters. This integration of DSR into ASP means that logical rules determine which spatial relationships must hold, while the spatial level ensures these relationships can be geometrically realized through coordinate assignments.
After generating a valid ASP program for the scenario’s context, GPT-4 is called again to add the question and options. Each option becomes an ‘answer rule’ that succeeds if the option is true. The system then uses Clingo to find stable models, ensuring that only options validated across all possible models are considered correct answers. This cautious reasoning mode helps address limitations where LLMs might struggle to consider all valid interpretations.
Experimental Validation
Embodied-LM was evaluated on two types of problems:
- LogicalDeduction Dataset (PATH Schema): This dataset involves multiple-choice questions requiring deductive reasoning about ordered objects. These problems naturally activate the PATH schema, mapping temporal sequences, spatial arrangements, or ordinal rankings to positional relations. For example, “older than” might be interpreted as “to the left of” on a timeline. The system achieved 91% accuracy on this dataset, demonstrating competitive performance compared to other neurosymbolic approaches.
- Zebra Puzzles (CONTAINER Schema): These are more complex reasoning scenarios where diverse relationships (like living in a house, drinking a beverage, owning a pet) are mapped to the CONTAINER image schema. The system successfully encoded problem constraints using spatial predicates like ‘in_pr’ (inside point-rectangle) and ‘left_rr’ (left rectangle-rectangle). It could find multiple possible models and consistently identify the correct answer through cautious reasoning.
Also Read:
- Building Robots with Spatial Awareness: A Deep Dive into Scene Understanding and Reasoning
- Unlocking LLM Reasoning: The Critical Role of Formal Languages in Neurosymbolic AI
Key Advantages and Future Directions
The research demonstrates that AI systems can reason using schematic structures in a way that parallels human cognitive processes. A significant advantage is that a single image schema can ground multiple types of information across different problem domains, providing a universal schematic language. This approach also offers enhanced interpretability, as the generated ASP program is directly inspectable, allowing for diagnosis and correction of errors.
While the current implementation focuses on spatial primitives, it lays a computational foundation for incorporating more complex schematic structures, including spatio-temporal and force-dynamic primitives. This could enable the system to tackle classic AI problems like the Tower of Hanoi or Blocks World scenarios. The researchers believe that further psychological research comparing human and system-generated schematic representations could provide valuable insights for refining these mapping challenges.
For more details, you can read the full research paper here.
