Current foundation models, such as GPT-3.5-Turbo and CLIP, face several limitations when solving multimodal STEM problems:
Limited Understanding of Visual Information: These models struggle to effectively interpret and utilize visual data in conjunction with textual information, hindering their ability to solve complex multimodal problems that require a deep understanding of both modalities.
Lack of Domain-Specific Knowledge: Foundation models may lack specialized knowledge in STEM fields, leading to inaccuracies in answering subject-specific questions that require domain expertise.
Inadequate Reasoning Abilities: These models may not possess advanced reasoning capabilities necessary for solving intricate STEM problems that involve logical deductions, critical thinking, and abstract reasoning.
To address these limitations, novel model architectures and training techniques can be implemented:
Hybrid Models: Develop hybrid models that combine the strengths of language models with vision models to enhance multimodal understanding and reasoning abilities.
Curriculum Learning: Implement curriculum learning techniques to train models progressively on increasingly complex STEM tasks, allowing them to build foundational knowledge before tackling more advanced problems.
Fine-Tuning Strategies: Utilize fine-tuning approaches that focus on specific STEM domains to enhance the models' proficiency in subject-specific tasks and improve their performance on STEM-related challenges.
Knowledge Distillation: Employ knowledge distillation methods to transfer domain-specific knowledge from expert systems or human teachers to the models, enhancing their understanding of complex STEM concepts.
By incorporating these advancements in model architectures and training methodologies, foundation models can overcome their limitations and become more proficient in solving multimodal STEM problems effectively.