While language models (LMs) like GPT-4o provide powerful capabilities for design generation, several limitations exist:
Contextual Understanding: LMs may struggle with understanding the full context of a design prompt, especially when it involves nuanced specifications or complex relationships between components. To address this, Blox-Net could incorporate multi-modal inputs, combining text with visual data (e.g., images of existing designs) to provide richer context for the model.
Ambiguity in Language: Natural language can be ambiguous, leading to misinterpretations in design generation. Implementing a feedback loop where the model iteratively refines its output based on user feedback or additional clarifications could help mitigate this issue.
Limited Knowledge of Physical Constraints: LMs may not inherently understand the physical constraints of materials and assembly processes. Integrating physics-based simulations into the design generation process would allow the model to generate designs that are not only conceptually sound but also physically feasible.
Scalability of Design Complexity: As design complexity increases, the computational resources required for LMs can become prohibitive. Optimizing the model architecture for efficiency and exploring techniques like model distillation could help maintain performance while reducing resource consumption.
Lack of Domain-Specific Knowledge: LMs may lack the specialized knowledge required for certain design domains. Training the model on domain-specific datasets or incorporating expert systems that provide additional guidance could enhance the model's capabilities in generating relevant designs.
By addressing these limitations, Blox-Net could improve its design generation capabilities, leading to more reliable and innovative outputs that meet user needs across various applications.