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Related Questions
- How do LLMs currently handle embodiment in language processing, and what limitations do they have in this regard?
- Can LLMs be retrained to better account for the role of sensorimotor experiences in shaping human cognition and language use?
- What are the key challenges in rephrasing or retraining LLMs to incorporate embodied cognition principles, and how can they be addressed?
- How do LLMs currently handle multimodal information, such as visual or auditory inputs, and can they be improved to better integrate embodiment?
- What are the implications of retraining LLMs to better account for embodiment on their performance in tasks such as language translation or question answering?
- Can LLMs be designed to learn from embodied experiences, such as those gained through robotics or virtual reality, and how might this impact their language understanding?
- What are the potential benefits and limitations of retraining LLMs to better account for embodiment, and how might they impact the development of more human-like AI systems?
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