Summary of Systematic Analysis Of Llm Contributions to Planning: Solver, Verifier, Heuristic, by Haoming Li et al.
Systematic Analysis of LLM Contributions to Planning: Solver, Verifier, Heuristic
by Haoming Li, Zhaoliang Chen, Songyuan Liu, Yiming Lu, Fei Liu
First submitted to arxiv on: 12 Dec 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 This paper investigates the role of large language models (LLMs) in solving planning problems. It evaluates how LLMs perform as problem solvers, solution verifiers, and heuristic guidance for intermediate solutions. The results show that while LLMs struggle to generate correct plans initially, they excel at providing feedback signals for incomplete solutions using comparative heuristic functions. This framework provides insights into designing better tree-search algorithms that leverage LLMs to tackle various planning and reasoning tasks. Additionally, the paper proposes a novel benchmark to assess an LLM’s ability to learn user preferences on-the-fly, with wide applications in practical settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are really smart computers that can help us solve problems. This research looks at how these models do when we use them to find solutions, check if they’re correct, and give hints to make them better. What the researchers found is that while these models aren’t great at coming up with perfect plans right away, they’re actually very good at giving feedback to help us improve our ideas. This helps us understand how to create even better computer programs that can solve lots of different problems. The study also proposes a new way to test how well these models can learn what people want on the fly. |
