Summary of Flowmind: Automatic Workflow Generation with Llms, by Zhen Zeng et al.
FlowMind: Automatic Workflow Generation with LLMs
by Zhen Zeng, William Watson, Nicole Cho, Saba Rahimi, Shayleen Reynolds, Tucker Balch, Manuela Veloso
First submitted to arxiv on: 17 Mar 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 paper introduces a novel approach called FlowMind that leverages Large Language Models (LLMs) to address the limitations of Robotic Process Automation (RPA) in handling spontaneous or unpredictable tasks. Specifically, FlowMind uses a generic prompt recipe for lectures to ground LLM reasoning with reliable APIs, mitigating hallucinations and ensuring data integrity and confidentiality. The system also presents high-level descriptions of auto-generated workflows, enabling users to inspect and provide feedback effectively. To evaluate the performance of FlowMind, the paper introduces NCEN-QA, a new dataset in finance for benchmarking question-answering tasks from N-CEN reports on funds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FlowMind is a new way to make computers do more helpful things without needing direct human interaction. It uses special language models that can understand and generate text, like chatbots. The problem with these models is that they often don’t make sense or provide incorrect information. FlowMind fixes this by using a “recipe” for the language model’s instructions, which ensures it gets the right information from reliable sources. This keeps sensitive data safe and secure. Additionally, FlowMind provides users with a summary of what the computer has done, so they can review and give feedback. |
Keywords
» Artificial intelligence » Language model » Prompt » Question answering
