title: LawFlow : Collecting and Simulating Lawyers' Thought Processes
summary: Legal practitioners, particularly those early in their careers, face complex,
  high-stakes tasks that require adaptive, context-sensitive reasoning. While AI
  holds promise in supporting legal work, current datasets and models are
  narrowly focused on isolated subtasks and fail to capture the end-to-end
  decision-making required in real-world practice. To address this gap, we
  introduce LawFlow, a dataset of complete end-to-end legal workflows collected
  from trained law students, grounded in real-world business entity formation
  scenarios. Unlike prior datasets focused on input-output pairs or linear chains
  of thought, LawFlow captures dynamic, modular, and iterative reasoning
  processes that reflect the ambiguity, revision, and client-adaptive strategies
  of legal practice. Using LawFlow, we compare human and LLM-generated workflows,
  revealing systematic differences in structure, reasoning flexibility, and plan
  execution. Human workflows tend to be modular and adaptive, while LLM workflows
  are more sequential, exhaustive, and less sensitive to downstream implications.
  Our findings also suggest that legal professionals prefer AI to carry out
  supportive roles, such as brainstorming, identifying blind spots, and surfacing
  alternatives, rather than executing complex workflows end-to-end. Building on
  these findings, we propose a set of design suggestions, rooted in empirical
  observations, that align AI assistance with human goals of clarity,
  completeness, creativity, and efficiency, through hybrid planning, adaptive
  execution, and decision-point support. Our results highlight both the current
  limitations of LLMs in supporting complex legal workflows and opportunities for
  developing more collaborative, reasoning-aware legal AI systems. All data and
  code are available on our project page
  (https://minnesotanlp.github.io/LawFlow-website/).
paper_authors._id: 681140d7f8ca0d9acb1690a9
paper_authors.name: Debarati Das
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-02T06:37:41.458Z
paper_authors._id: 60f8a014595289005b768233
paper_authors.avatarUrl: /avatars/73b1ed0aa6728fa04607a99fb2eb7317.svg
paper_authors.fullname: Debarati Das
paper_authors.type: user
paper_authors.user: das00015
paper_authors._id: 681140d7f8ca0d9acb1690aa
paper_authors.name: Khanh Chi Le
paper_authors._id: 681140d7f8ca0d9acb1690ab
paper_authors.name: Ritik Sachin Parkar
paper_authors._id: 681140d7f8ca0d9acb1690ac
paper_authors.name: Karin De Langis
paper_authors._id: 681140d7f8ca0d9acb1690ad
paper_authors.name: Brendan Madson
paper_authors._id: 681140d7f8ca0d9acb1690ae
paper_authors.name: Chad M. Berryman
paper_authors._id: 681140d7f8ca0d9acb1690af
paper_authors.name: Robin M. Willis
paper_authors._id: 681140d7f8ca0d9acb1690b0
paper_authors.name: Daniel H. Moses
paper_authors._id: 681140d7f8ca0d9acb1690b1
paper_authors.name: Brett McDonnell
paper_authors._id: 681140d7f8ca0d9acb1690b2
paper_authors.name: Daniel Schwarcz
paper_authors._id: 681140d7f8ca0d9acb1690b3
paper_authors.name: Dongyeop Kang
paper_title: LawFlow : Collecting and Simulating Lawyers' Thought Processes
paper_summary: Legal practitioners, particularly those early in their careers, face complex,
  high-stakes tasks that require adaptive, context-sensitive reasoning. While AI
  holds promise in supporting legal work, current datasets and models are
  narrowly focused on isolated subtasks and fail to capture the end-to-end
  decision-making required in real-world practice. To address this gap, we
  introduce LawFlow, a dataset of complete end-to-end legal workflows collected
  from trained law students, grounded in real-world business entity formation
  scenarios. Unlike prior datasets focused on input-output pairs or linear chains
  of thought, LawFlow captures dynamic, modular, and iterative reasoning
  processes that reflect the ambiguity, revision, and client-adaptive strategies
  of legal practice. Using LawFlow, we compare human and LLM-generated workflows,
  revealing systematic differences in structure, reasoning flexibility, and plan
  execution. Human workflows tend to be modular and adaptive, while LLM workflows
  are more sequential, exhaustive, and less sensitive to downstream implications.
  Our findings also suggest that legal professionals prefer AI to carry out
  supportive roles, such as brainstorming, identifying blind spots, and surfacing
  alternatives, rather than executing complex workflows end-to-end. Building on
  these findings, we propose a set of design suggestions, rooted in empirical
  observations, that align AI assistance with human goals of clarity,
  completeness, creativity, and efficiency, through hybrid planning, adaptive
  execution, and decision-point support. Our results highlight both the current
  limitations of LLMs in supporting complex legal workflows and opportunities for
  developing more collaborative, reasoning-aware legal AI systems. All data and
  code are available on our project page
  (https://minnesotanlp.github.io/LawFlow-website/).
paper_ai_summary: LawFlow, a dataset of complete end-to-end legal workflows, reveals differences between human and LLM reasoning in legal tasks, suggesting AI should support rather than execute complex workflows.
paper_ai_keywords: narrowly focused datasets LawFlow end-to-end legal workflows dynamic reasoning processes modular reasoning iterative reasoning ambiguity revision client-adaptive strategies LLM-generated workflows sequential reasoning exhaustive reasoning decision-point support hybrid planning adaptive execution reasoning-aware legal AI systems