Summary of One Step at a Time: Language Agents Are Stepwise Planners, by Minh Nguyen et al.
One STEP at a time: Language Agents are Stepwise Planners
by Minh Nguyen, Ehsan Shareghi
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed STEP framework aims to enhance the planning capabilities of large language models by learning from previous experiences and adapting to new situations. The framework consists of four interconnected components: Planner, Executor, Evaluator, and Memory. These components work together to break down complex tasks into subtasks, generate action candidates, evaluate their feasibility, and store experiences for future decision-making. In the ScienceWorld benchmark, STEP outperforms state-of-the-art models, achieving an overall score of 67.4 and completing 12 out of 18 tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The STEP framework is a new approach to planning in language agents that can learn from past experiences and adapt to new situations. This means that language agents using the STEP framework can get better at solving complex problems over time, which could be very useful for things like natural language processing or artificial intelligence. The framework has four main parts: Planner, Executor, Evaluator, and Memory. Each part works together with the others to help the agent make good decisions. |
Keywords
* Artificial intelligence * Natural language processing
