AI Researchers have been working on developing AI systems that can communicate in natural language effectively, similar to how humans do. However, existing models, like Eliza from 1966, lack true understanding and can easily be tricked by humans. Large language models (LLMs) such as GPT-4 and ChatGPT have exceeded expectations but still fall short when it comes to comprehension.
LLMs are designed to generate believable word sequences based on context, rather than truly understanding the meaning behind the words. This poses a challenge when it comes to tasks like solving math problems or planning tasks that require knowledge of the outside world. The conventional approach of including all possible scenarios in their training data is not practical.
To address this issue, researchers from UT Austin and the State University of New York have introduced a method called LLM+P. This approach allows an LLM to:
1. Generate a problem description suitable for a general-purpose planner.
2. Solve the problem using the planner.
3. Convert the planner’s output back into natural language.
The goal of this research is to provide LLMs with accurate solutions to planning problems without modifying the LLM itself. The researchers have conducted empirical analyses, which show that LLM+P can accurately solve a wide range of planning problems. This approach can also be applied to other types of problems if there is a reliable solver available.
For more information about this research, you can check out the paper and the GitHub link. Don’t forget to join our ML SubReddit, Discord Channel, and Email Newsletter for the latest AI research news and projects. If you have any questions or suggestions, feel free to reach out to us at [email protected]
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This article was authored by Aneesh Tickoo, a consulting intern at MarktechPost, who is currently pursuing a degree in Data Science and Artificial Intelligence from the Indian Institute of Technology (IIT), Bhilai. Aneesh is passionate about machine learning, particularly image processing, and enjoys collaborating on exciting projects.