Long-Form Methodical Writing System

A Long-Form Methodical Writing System is a structured writing system that requires the generation of detailed, organized, and domain-specific content to accomplish complex objectives across various professional fields.

• AKA Structured Long-Form Writing System.
• Context:
  • It can involve composing comprehensive documents such as lesson plans, clinical reports, technical manuals, or legal briefs that follow specific formats and standards.
  • It can require adherence to domain-specific conventions, including terminology, structure, and regulatory guidelines.
  • It can necessitate the integration of various information sources, critical analysis, and methodical reasoning to produce coherent and purposeful content.
  • It can be time-intensive, often involving iterative drafting and revision processes to meet professional quality standards.
  • ...
• Example(s):
  • Drafting a differential diagnosis report in the medical field, detailing patient symptoms, possible conditions, and recommended tests.
  • Creating a detailed lesson plan for educators, outlining objectives, materials, activities, and assessment methods.
  • Developing a technical specification document in engineering, describing system requirements, design considerations, and implementation steps.
  • ...
• Counter-Example(s):
  • Writing informal blog posts or personal essays that do not require structured formatting or domain-specific knowledge.
  • Generating creative writing pieces, such as short stories or poems, which prioritize artistic expression over structured content.
  • Composing brief summaries or abstracts that lack the depth and complexity characteristic of long-form methodical writing.
  • ...
• See: Long-Form Methodical Writing Benchmarking Task, DoLoMiTes, Domain-Specific Natural Language Generation Task, Professional Writing Assistance, Technical Documentation, Domain-Specific Writing Task, Professional Writing Task.

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References

2024a

• (Malaviya et al., 2024) ⇒ C. Malaviya, P. Agrawal, K. Ganchev, P. Srinivasan, F. Huot, J. Berant, M. Yatskar, D. Das, M. Lapata, & C. Alberti. (2024). "Dolomites: Domain-Specific Long-Form Methodical Tasks". In: Transactions of the Association for Computational Linguistics.
  • QUOTE: Dolomites is a long-form benchmark for evaluating language models on realistic domain-specific writing tasks. Experts in various fields routinely perform methodical writing tasks to plan, organize, and report their work. From a clinician writing a differential diagnosis for a patient, to a teacher writing a lesson plan for students, these tasks are pervasive, requiring to methodically generate structured long-form output for a given input. We develop a typology of methodical tasks structured in the form of a task objective, procedure, input, and output, and introduce DoLoMiTes, a novel benchmark with specifications for 519 such tasks elicited from hundreds of experts from across 25 fields. Our benchmark further contains specific instantiations of methodical tasks with concrete input and output examples (1,857 in total) which we obtain by collecting expert revisions of up to 10 model-generated examples of each task. We use these examples to evaluate contemporary language models highlighting that automating methodical tasks is a challenging long-form generation problem, as it requires performing complex inferences, while drawing upon the given context as well as domain knowledge.

2024b

• (Google DeepMind, 2024) ⇒ Google DeepMind. (2024). "DoLoMiTes: Domain-Specific Long-Form Methodical Tasks".
  • QUOTE: This repository includes data for the DoLoMiTes (Domain-Specific Long-Form Methodical Tasks) evaluation benchmark, described in our paper. The benchmark data is available in JSONL format and includes 519 task descriptions provided by experts, validation labels by independent experts, and 1,857 examples of the tasks post-edited by experts. The development set contains 830 examples with reference outputs and the test set contains 1,037 examples without reference outputs, supporting rigorous evaluation of long-form generation models.

2024c

• (Dolomites Benchmark Team, 2024) ⇒ Dolomites Benchmark Team. (2024). "Dolomites: Domain-Specific Long-Form Methodical Tasks".
  • QUOTE: The Dolomites benchmark consists of 519 expert-authored, long-form task descriptions spanning 25 fields, with 1,857 examples that instantiate the tasks with plausible inputs and outputs. Tasks are challenging and require domain expertise. Independent judgements of task validity and societal implications of using language models as writing assistants for these tasks are included. The benchmark is an ongoing effort and is being actively expanded.

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