EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 5·5 min read
Position: LLMs Can't Jump
TL;DR
Recent discussions highlight that large language models (LLMs) struggle with tasks requiring physical reasoning, such as jumping. This limitation raises questions about their applicability in real-world scenarios.
✦ Why It Matters
Consider incorporating physical reasoning frameworks into your AI projects to address LLM limitations.
Key Takeaways
- Evaluate LLMs for text generation, not physical tasks.
- Explore hybrid models combining LLMs with physics engines.
- Test LLMs in controlled environments for physical reasoning.
- Stay updated on advancements in LLM capabilities.
Full Summary
Recent discussions reveal that Large Language Models (LLMs) like GPT-3 and others excel in processing and generating text but falter in tasks that require physical reasoning, such as predicting the outcome of physical interactions. For instance, when asked to solve problems involving gravity or motion, LLMs often provide incorrect or nonsensical answers.
This discrepancy arises because LLMs lack a grounded understanding of the physical world, relying solely on patterns in text data rather than experiential knowledge. Researchers have noted that while LLMs can generate plausible narratives, they cannot accurately simulate real-world physics scenarios.
This limitation suggests that integrating physical reasoning capabilities into LLMs could enhance their utility in applications requiring real-world interactions, such as robotics or simulation-based training.