Summary of Robust Planning with Compound Llm Architectures: An Llm-modulo Approach, by Atharva Gundawar et al.
Robust Planning with Compound LLM Architectures: An LLM-Modulo Approach
by Atharva Gundawar, Karthik Valmeekam, Mudit Verma, Subbarao Kambhampati
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 LLM-Modulo framework combines a Large Language Model (LLM) with sound verifiers that validate its output, ensuring that every generated output is correct. This compound architecture addresses limitations in previous prompt engineering techniques, which can be unreliable and unpredictable. The evaluation demonstrates significant performance gains across four scheduling domains using various models. Modifications to the base configuration also impact overall system performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to make Large Language Models (LLMs) better at planning and scheduling tasks. Current methods can work well in certain situations, but they’re not reliable or predictable. The new approach combines an LLM with special “verifiers” that check the output for mistakes and ask the LLM to try again if it’s wrong. This ensures that the system always produces accurate results. The paper shows that this method works well across different scheduling tasks and provides some tips on how to make it even better. |
Keywords
» Artificial intelligence » Large language model » Prompt
