Summary of Can Llms Plan Paths with Extra Hints From Solvers?, by Erik Wu and Sayan Mitra
Can LLMs plan paths with extra hints from solvers?
by Erik Wu, Sayan Mitra
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Robotics (cs.RO)
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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 medium-difficulty summary: This paper enhances Large Language Model (LLM) performance in solving robotic planning tasks by integrating solver-generated feedback. Four strategies are explored, including visual feedback, fine-tuning, and evaluation across 110 problems (10 standard and 100 randomly generated). Results show improved LLM ability to solve moderately difficult problems, but harder ones remain unsolved. The study analyzes the effects of different hinting strategies and planning tendencies of three evaluated LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A low-difficulty summary: This paper helps computers better plan for tasks like a robot might do. It looks at how we can make computers smarter by giving them hints or feedback to solve problems. The researchers tried four different ways of giving these hints, and tested it on many different kinds of planning problems. They found that the hints helped the computer solve some problems, but not all of them were easy enough. The study shows what works best for each type of hint and which computers are better at solving certain types of problems. |
Keywords
» Artificial intelligence » Fine tuning » Large language model
