LLMs Can Plan Only If We Tell Them
This addresses the challenge of enabling LLMs to plan effectively without costly feedback mechanisms, which is crucial for applications in robotics and AI systems.
The paper tackles the problem of LLMs' limited autonomous planning capabilities by introducing AoT+, which achieves state-of-the-art results on planning benchmarks like Blocksworld, outperforming prior methods and human baselines without external support.
Large language models (LLMs) have demonstrated significant capabilities in natural language processing and reasoning, yet their effectiveness in autonomous planning has been under debate. While existing studies have utilized LLMs with external feedback mechanisms or in controlled environments for planning, these approaches often involve substantial computational and development resources due to the requirement for careful design and iterative backprompting. Moreover, even the most advanced LLMs like GPT-4 struggle to match human performance on standard planning benchmarks, such as the Blocksworld, without additional support. This paper investigates whether LLMs can independently generate long-horizon plans that rival human baselines. Our novel enhancements to Algorithm-of-Thoughts (AoT), which we dub AoT+, help achieve state-of-the-art results in planning benchmarks out-competing prior methods and human baselines all autonomously.