title: FlowMind: Automatic Workflow Generation with LLMs
summary: The rapidly evolving field of Robotic Process Automation (RPA) has made
  significant strides in automating repetitive processes, yet its effectiveness
  diminishes in scenarios requiring spontaneous or unpredictable tasks demanded
  by users. This paper introduces a novel approach, FlowMind, leveraging the
  capabilities of Large Language Models (LLMs) such as Generative Pretrained
  Transformer (GPT), to address this limitation and create an automatic workflow
  generation system. In FlowMind, we propose a generic prompt recipe for a
  lecture that helps ground LLM reasoning with reliable Application Programming
  Interfaces (APIs). With this, FlowMind not only mitigates the common issue of
  hallucinations in LLMs, but also eliminates direct interaction between LLMs and
  proprietary data or code, thus ensuring the integrity and confidentiality of
  information - a cornerstone in financial services. FlowMind further simplifies
  user interaction by presenting high-level descriptions of auto-generated
  workflows, enabling users to inspect and provide feedback effectively. We also
  introduce NCEN-QA, a new dataset in finance for benchmarking question-answering
  tasks from N-CEN reports on funds. We used NCEN-QA to evaluate the performance
  of workflows generated by FlowMind against baseline and ablation variants of
  FlowMind. We demonstrate the success of FlowMind, the importance of each
  component in the proposed lecture recipe, and the effectiveness of user
  interaction and feedback in FlowMind.
paper_authors._id: 6627228c663d787f5ac3d585
paper_authors.name: Zhen Zeng
paper_authors.status: claimed_verified
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paper_authors.fullname: Zhen zeng
paper_authors.type: user
paper_authors.user: zengzhen
paper_authors._id: 6627228c663d787f5ac3d586
paper_authors.name: William Watson
paper_authors.status: extracted_pending
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paper_authors.fullname: William Watson
paper_authors.type: user
paper_authors.user: billwat
paper_authors._id: 6627228c663d787f5ac3d587
paper_authors.name: Nicole Cho
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-04-30T07:55:27.148Z
paper_authors._id: 6630a380b6d15534202ab83f
paper_authors.avatarUrl: /avatars/69af9465544b668c65ad4d9866fbfd06.svg
paper_authors.fullname: Nicole Cho
paper_authors.type: user
paper_authors.user: NicoleCho
paper_authors._id: 6627228c663d787f5ac3d588
paper_authors.name: Saba Rahimi
paper_authors._id: 6627228c663d787f5ac3d589
paper_authors.name: Shayleen Reynolds
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-04-23T16:48:45.963Z
paper_authors._id: 6627e5f48197af66dcb0fe68
paper_authors.avatarUrl: /avatars/99044c49b67daf7fb0c54c0d81a73f11.svg
paper_authors.fullname: Reynolds
paper_authors.type: user
paper_authors.user: Shayleen
paper_authors._id: 6627228c663d787f5ac3d58a
paper_authors.name: Tucker Balch
paper_authors._id: 6627228c663d787f5ac3d58b
paper_authors.name: Manuela Veloso
paper_title: FlowMind: Automatic Workflow Generation with LLMs
paper_summary: The rapidly evolving field of Robotic Process Automation (RPA) has made
  significant strides in automating repetitive processes, yet its effectiveness
  diminishes in scenarios requiring spontaneous or unpredictable tasks demanded
  by users. This paper introduces a novel approach, FlowMind, leveraging the
  capabilities of Large Language Models (LLMs) such as Generative Pretrained
  Transformer (GPT), to address this limitation and create an automatic workflow
  generation system. In FlowMind, we propose a generic prompt recipe for a
  lecture that helps ground LLM reasoning with reliable Application Programming
  Interfaces (APIs). With this, FlowMind not only mitigates the common issue of
  hallucinations in LLMs, but also eliminates direct interaction between LLMs and
  proprietary data or code, thus ensuring the integrity and confidentiality of
  information - a cornerstone in financial services. FlowMind further simplifies
  user interaction by presenting high-level descriptions of auto-generated
  workflows, enabling users to inspect and provide feedback effectively. We also
  introduce NCEN-QA, a new dataset in finance for benchmarking question-answering
  tasks from N-CEN reports on funds. We used NCEN-QA to evaluate the performance
  of workflows generated by FlowMind against baseline and ablation variants of
  FlowMind. We demonstrate the success of FlowMind, the importance of each
  component in the proposed lecture recipe, and the effectiveness of user
  interaction and feedback in FlowMind.
paper_ai_summary: FlowMind uses Large Language Models with a generic prompt recipe to generate automatic workflows, addressing spontaneous tasks and ensuring data integrity, and it is evaluated using a new financial dataset NCEN-QA.
paper_ai_keywords: Large Language Models Generative Pretrained Transformer hallucinations API user interaction feedback NCEN-QA