Summary of Langsuite: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments, by Zixia Jia et al.
LangSuitE: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments
by Zixia Jia, Mengmeng Wang, Baichen Tong, Song-Chun Zhu, Zilong Zheng
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces LangSuitE, a novel testbed for evaluating Large Language Models (LLMs) as few-shot or zero-shot embodied agents in dynamic interactive environments. The authors present six representative embodied tasks in textual embodied worlds, which adapt to diverse environments without requiring multiple simulation engines. LangSuitE also evaluates an agent’s capacity to develop “internalized world knowledge” through embodied observations and allows easy customization of communication and action strategies. To address the embodiment challenge, a novel chain-of-thought (CoT) schema, EmMem, is proposed, which summarizes embodied states w.r.t. history information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special testbed for computers that can understand language to see how well they work in situations where they need to take actions based on what they’ve learned. The test includes six different tasks that simulate real-life scenarios, like ordering food or asking for directions. The computer program is tested in different environments and situations to see if it can learn from experience and make good decisions. |
Keywords
» Artificial intelligence » Few shot » Zero shot
