Summary of Grounding Large Language Models in Embodied Environment with Imperfect World Models, by Haolan Liu et al.
Grounding Large Language Models In Embodied Environment With Imperfect World Models
by Haolan Liu, Jishen Zhao
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have achieved success in various applications but struggle when performing basic physical reasoning or robotics tasks due to a lack of real-world experience. To address this, we propose GLIMO, which uses proxy world models like simulators to collect training data. GLIMO incorporates an LLM agent-based data generator that creates high-quality instruction datasets with iterative self-refining and diverse question-answering seeds. Our approach improves the performance of strong open-source LLMs like LLaMA-3 by 2.04, 1.54, and 1.82 times across three benchmarks, competing with or surpassing larger models like GPT-4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper solves a problem with large language models (LLMs) that are good at many things but not very good at understanding the physical world. To help them learn about the real world, scientists created a new way to train LLMs using computer simulations. This approach improves the performance of these models by making them more knowledgeable and able to understand physical tasks. |
Keywords
» Artificial intelligence » Gpt » Llama » Question answering
