Summary of Towards Autonomous Agents: Adaptive-planning, Reasoning, and Acting in Language Models, by Abhishek Dutta and Yen-che Hsiao
Towards Autonomous Agents: Adaptive-planning, Reasoning, and Acting in Language Models
by Abhishek Dutta, Yen-Che Hsiao
First submitted to arxiv on: 12 Aug 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 The proposed in-context learning algorithm enables the development of autonomous decision-making language agents that can self-correct and improve their problem-solving capabilities. The algorithm involves a language agent continuously attempting to solve the same task, with each failure prompting self-correction. In a text-based game environment, the gemma-2-9b-it language model using this method successfully completed two tasks initially failed in the first attempt. This highlights the effectiveness of the approach in enhancing the problem-solving capabilities of a single language model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The algorithm helps develop autonomous language agents that can learn and improve over time. It’s like a game where the agent tries to solve a task, fails, and then adjusts its strategy to try again. In this case, the agent was able to successfully complete two tasks it initially struggled with. This is important because it could lead to more advanced autonomous agents in the future. |
Keywords
» Artificial intelligence » Language model » Prompting
