LawFlow: Collecting and Simulating Lawyers’ Thought Processes on Business Formation Case Studies
Authors: Debarati Das
Khanh Chi Le
Ritik Sachin Parkar
Karin De Langis
Brendan Madson
Chad M. Berryman
Robin M Willis
Daniel H Moses
Brett McDonnell
Daniel Schwarcz
Dongyeop Kang
Venue: COLM 2025 (2025)
Keywords: pretraining data; synthetic data; reasoning; legal perspectives; legal drafting; LLM; chain of thought
TL;DR: LawFlow provides a dataset capturing complex legal workflows for small business-entity formation, highlighting multi-stage reasoning, client communication, and iterative revisions. It reveals human workflows’ flexibility and informs more adaptive AI.

Abstract: 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. 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/)


Reviews:
[{"review_id": "SwRPcTZqYw", "reviewer": "Reviewer_PPMy", "summary": "This work describes LawFlow, a dataset aimed at supporting LLM-based systems in the task of defining  complete end-to-end legal workflows, in contrast to current approaches that typically focus on different sub-tasks.  The framework is based on real workflows extracted by trained law students that cover from initial intake to operating agreement drafting, and are decomposed into modular subtasks (client elicitation, document review, iterative editing, ...).\n\nThe paper describes the dataset in detail and shows a comparison between LLM and human generated legal workflows, analysing the differences and detecting improvement points.\n\nThe paper is well written and motivated and addresses an interesting topic. AI applications in the legal domain have gained enormous popularity in the last decade. However, there are still gaps and this work comes to fill one of them. Particularly, it treats the legal process not atomically or focusing on the outcomes only, but as a whole (what they call \"chain-of-decisions\"). This study has served to highlight the limitations of LLM-based approaches, paving the way to future approaches that will add more flexibility and revisioning capabilities. \n\nThe workflow preparation and annotation process is described in detail, as well as the comparative (human vs LLMs) analysis. \n\nThe paper describes still exploratory, non-conclusive, work, but progresses in the right direction and illustrates interesting findings in automatic workflow construction.", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "* The paper is well written and motivated and addresses an interesting topic. \n* The workflow preparation and annotation process is described in detail, as well as the comparative (human vs LLMs) analysis.", "reasons_to_reject": "* The paper describes still exploratory, non-conclusive, work\n* More technical details could be given about the LLM-based workflow preparation+ execution. For instance, they mention using two reasoning models, but not how they are combined. \n* I miss a reference to the degree of competence of the involved law students. It can make a huge difference if they are undergraduate, master students, or PhD students. I also miss the number of students involved in the dataset preparation. \n* The study grounds on workflows for \"operating agreement\" preparation. I miss a discussion on how to generalise it to other type of legal activities.", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "mpmWO4wDJM", "reviewer": "Reviewer_dgKy", "summary": "This work presents LawFlow, a new dataset of end-to-end legal workflows. Law faculty and students construct an export-informed task plan that captures the workflow at three levels of granularity. The task plan is then used to create workflows in a simulated setting, where law students conducted structured roleplaying, with computer science students acting as clients. Data collection was facilitated by the LawFlow tool that captures task annotations (mappings to expert plan), notes, browser and vector search for legal and template search, and document editing for legal drafting. 20 simulated scenarios were created, producing 10 memos and 8 finalized agreements.\n\nThe resulting data is used to compare the ability of humans and new reasoning models (o1 and r1) on legal workflows. The findings demonstrate that LLM-generated workflows are linear, lacking backtracking or goal-realignment. In contrast, human generated workflows are modular and iterative.\n\nThe paper shows how LawFlow can support the integration of LLMs into legal reasoning through 1) a fine-tuned model that predicts unlikely task steps (without assuming a linear path), and 2) analysing workflows to identify meta-decision points (important decision points affecting the final deliverable).", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- This paper contributes a complex and novel dataset in an important domain. The dataset is created using domain experts, and a collections of tools are built to support dataset creation.\n\n- Multiple reasoning models (open and closed) are evaluated on the dataset with a detailed analysis comparing task planning given.\n\n- Two uses cases for AI integration into legal workflows are presented, motivated by the dataset, including a fine-tuned model for task prediction.\n\n- The paper is clearly written and presented, including a project page that gives a dataset overview.", "reasons_to_reject": "The paper appears to be formatted using a different template than the provided COLM one. Could the authors clarify whether they have modified the style?\n\nAside from the potential formatting issues, the paper could benefit from more detailed explanation in a few key areas:\n\n*Dataset complexity*\n\nThe dataset consists of 20 simulated scenarios, which on the surface n=20 appears small. I expect that as this is a comprehensive end-to-end data set, and when considering all the subtasks and artifacts, the complexity and scale is significant larger. The paper would benefit from a more comprehensive dataset overview (beyond counting nodes and tree depth). For example, what are the lengths of the generated agreements and memos, how many tool invocations where there, and what domains are the seed scenarios from?\n\n*Focus on workflow*\n\nThe focus of the paper is around workflow structure and adherence to planning, which is a crucial topic. Still, the output of the process could be a memo or agreement, presumably with some notion of correctness or legal rigour. Discussion of the quality of outputs would be worthwhile, even if providing a mechanism to automatically evaluate them is out of scope of this work. When interacting with legal professionals, did you get a sense of the quality of the LLM agreements?\n\n*Modelling Atypicality*\n\nThe paper shows how a fine-tuned LLM can be used to identify steps that diverge from typical human reasoning patterns. The methodology and evaluations is very brief. Was this model tuned using SFT or RL (could RL apply here?) The model was trained on eight human generated workflows. Are these from the 20 simulated scenarios? Presumably each training example is an action within a workflow. Across the 8 workflows, how many training examples were there? How accurate was the trained classifier? Could a LLM (like o1) perform this task more efficiently using an in context-only approach? Broadly speaking, this presentation of the classifier is insufficient to reproduce it, or understand its accuracy.", "questions_to_authors": "(115) How where the in-depth interviews with senior law faculty converted into the expert-informed task plan? Was there a thematic analysis of the interviews? Was AI used in this process?\n\n(145) One student was responsible for creating the set of realistic seed scenarios. What was the process of converting the anonymized cases into the scenarios, what was the selection criteria, and could having only one student perform this task introduce bias into the dataset?\n\n(179) 20 scenarios where collected, with 10 memos and 8 agreements. This suggests that some scenarios did not produce a memo or agreement. Why was this the case? Are these scenarios still useful – if so, what behaviour or patterns do they capture?\n\n(A.7) The appendix describes LLM-as-Judge but this was not mentioned in the main paper. Is this evaluating the quality of the workflow output? If so, what where the results?\n\n(A.8 b). Browser and Vector search where never used. Why? What does avg use mean? Did they prefer Google or Westlaw, rather than your tools?\n\n**Suggestions**\n\nThis work would benefit from an ethics statement given the real-word implications of AI integration into law, and the nature of this dataset targeting end-to-end tasks.", "extra_scores": {}}, {"review_id": "1U26uUc55e", "reviewer": "Reviewer_ZrPG", "summary": "The paper introduces Lawflow, capturing end-to-end worlkflows collected from trained law students on  business entity formation scenarios. Most of the prior datasets in legal NLP emphasize static input-output mappings or linear reasoning, LawFlow records the modular, iterative, and adaptive decision-making processes involved in real-world legal tasks mirroring how legal practitioners deal with ambiguity and revise plans over time.\n\nThe authors use law flow dataset to assess how human workflows with differ from LLM based workflows and observed differences in  identifying notable differences in structure, reasoning flexibility, and responsiveness to ambiguity. They notice while human workflows are modular and iterative, while LLMs follow rigid, linear sequences with limited capacity for revision. They also propose and demonstrate a workflow monitor that flags atypical steps based on deviance from human-like patterns, supporting reflective helpful behavior of these agents. They also identify decision points like early-stage note-taking and question-asking, where AI assistance is most impactful.\n\nThese insights can help further to developing interactive legal AI tools such as workflow visualizers and reflective agents to assist legal practitioners.", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- The LawFlow dataset is a good contribution to legal NLP by moving beyond siloed tasks to capture holistic legal reasoning processes which are iterative, modular and adaptive. \n- It is a timely and innovative contribution to legal AI. It takes seriously the complexity of legal reasoning and offers a dataset that foregrounds process over outcome. \n- Empirical comparisons between human and LLM workflows highlight structural and cognitive differences in a convincing manner, such as rigidity in LLM reasoning or over-execution of branches.\n- The comparative analysis with LLMs reveals both current limitations and opportunities for symbiotic human-AI systems which are seeing to increasing with the trend. While some technical and evaluative details could be fleshed out more clearly for easy grasping, the conceptual and dataset contributions are highly valuable and will likely inspire follow-up work in legal NLP and workflow modeling.", "reasons_to_reject": "There is no strong reasons, except some suggestions to improve the writing.", "questions_to_authors": "- This paper is focused primarily on business formation, the domain scope is still narrow. This is completely fine, given the challenging set of tasks lawyers deal with. However, I suggest authors to reflect that in title as case study on this or specific use case in title rather the current title which seem to appear to deal with all use cases.\n- While the paper mentions that the diversity across humans is possible for a given task, some analysis on it could be helpful and insightful.\n- More concrete examples of the LawFlow dataset entries would be helpful (e.g., excerpts of workflows, annotations) and explaining an end-to-end example would be beneficial for readers (even in appendix)\n- A.4 seems to be missing details, it has to be elaborated either in figure or in text.", "extra_scores": {}}]

Author rebuttal:

Meta-review: This work provides a useful evaluation framework, drawn from interactions with real law students to more realistically evaluate legal workflows. While still exploratory, reviewers agreed that the paper was a useful contribution to the community and recommended acceptance.
Decision: Accept