Summary of Revealing the Barriers Of Language Agents in Planning, by Jian Xie et al.
Revealing the Barriers of Language Agents in Planning
by Jian Xie, Kexun Zhang, Jiangjie Chen, Siyu Yuan, Kai Zhang, Yikai Zhang, Lei Li, Yanghua Xiao
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 A novel approach to autonomous planning is proposed, building upon the reasoning capabilities of large language models (LLMs). While LLMs have shown promise in generating reasonable solutions for specific tasks, they still lack human-level planning abilities. Even state-of-the-art models like OpenAI o1 achieve only 15.6% on complex real-world planning benchmarks. This highlights the need to understand the underlying issues hindering language agents from achieving human-level planning. By applying feature attribution studies, researchers identify two key factors limiting agent planning: the limited role of constraints and the diminishing influence of questions. Current strategies help mitigate these challenges but do not fully resolve them. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous planning has been a long-standing goal in artificial intelligence. Previously, early planning agents could deliver precise solutions for specific tasks, but they lacked generalization. Recently, large language models (LLMs) have rekindled interest in autonomous planning by automatically generating reasonable solutions. However, current LLMs still lack human-level planning abilities. To better understand what hinders these language agents from achieving human-level planning, researchers applied feature attribution studies and found that the limited role of constraints and diminishing influence of questions are key factors holding them back. |
Keywords
» Artificial intelligence » Generalization
