Summary of Skill Learning Using Process Mining For Large Language Model Plan Generation, by Andrei Cosmin Redis et al.
Skill Learning Using Process Mining for Large Language Model Plan Generation
by Andrei Cosmin Redis, Mohammadreza Fani Sani, Bahram Zarrin, Andrea Burattin
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
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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 The proposed novel approach integrates process mining techniques to improve large language models’ (LLMs) plan generation capabilities for complex tasks. By leveraging process discovery for skill acquisition, process models for skill storage, and conformance checking for skill retrieval, the method enhances text-based plan generation by enabling flexible skill discovery, parallel execution, and improved interpretability. The approach surpasses state-of-the-art accuracy baselines under specific conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can help generate plans for complex tasks, but they have limitations. This paper tries to solve these issues so that LLMs can be more useful for things like automation and decision-making. They do this by combining different techniques from process mining, which is a way of analyzing how processes work. The result is a new method that makes it easier to discover skills, execute plans in parallel, and understand what’s happening during the planning process. |
