Summary of Large Language Models Can Solve Real-world Planning Rigorously with Formal Verification Tools, by Yilun Hao et al.
Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Tools
by Yilun Hao, Yongchao Chen, Yang Zhang, Chuchu Fan
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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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 tackles the challenge of large language models (LLMs) in generating correct plans for complex multi-constraint planning problems. The authors propose an LLM-based planning framework that formalizes and solves these problems as constrained satisfiability problems, which are then consumed by sound and complete satisfiability solvers. The framework is tested on the TravelPlanner benchmark, achieving a success rate of 93.9% and strong zero-shot generalizability to new constraints and domains. Additionally, the framework can identify unsatisfiable queries and provide personalized modification suggestions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem with language models. They struggle to come up with good plans for complicated tasks that have many rules. The authors create a new way to use language models for planning that makes it easier to find good solutions. This works really well on a test case called TravelPlanner, and it can even handle new challenges that it hasn’t seen before. It’s also good at finding problems and suggesting ways to fix them. |
Keywords
» Artificial intelligence » Zero shot
