Automated Domain-Specific Writing System

A Automated Domain-Specific Writing System is an automated writing system which supports automated domain-specific writing tasks (for domain-specific written artifacts).

• AKA: Domain Writing Automation, Specialized Content Generator, Domain-Specific Content Automation.
• Context:
  • It can typically process Domain Knowledge with knowledge representation techniques.
  • It can typically generate Content Output with template-based formatting rules.
  • It can typically incorporate Subject Expertise with domain ontology integration.
  • It can typically maintain Formatting Consistency through rule-based validation mechanisms.
  • It can typically handle Specialized Terminology through domain vocabulary management.
  • ...
  • It can often facilitate Content Structuring through information architecture principles.
  • It can often provide Contextual Linking through semantic relationship analysis.
  • It can often implement Style Enforcement through formatting rule application.
  • It can often support Knowledge Organization through taxonomy implementation.
  • It can often adapt Content Generation through domain-specific parameter configuration.
  • ...
  • It can range from being a Simple Template System to being a Full Knowledge Generation Platform, depending on its implementation complexity.
  • It can range from being a Single-Domain Specialist to being a Multi-Domain Generalist, depending on its knowledge scope.
  • It can range from being a Basic Text Generator to being an Advanced Content Architecture, depending on its capability level.
  • It can range from being a Rule-Based System to being a Learning-Based System, depending on its adaptation capability.
  • ...
  • It can have Quality Assurance Mechanisms for content validation processes.
  • It can have Audience Adaptation Features for content customization requirements.
  • It can have Structured Output Formats for knowledge representation purposes.
  • It can have Version Control Integration for content evolution tracking.
  • ...
  • It can be Domain Constrained during specialized knowledge production.
  • It can be Rule Governed during content generation procedures.
  • It can be Template Driven during structured document creation.
  • It can be Organizationally Aligned during corporate knowledge management.
  • ...
  • It can be an AI-driven tool that generates domain-specific content by leveraging natural language processing and domain knowledge bases to ensure accuracy and compliance.
  • It can streamline document creation in fields like legal contract drafting, medical report writing, and technical documentation.
  • It can utilize fine-tuned language models (e.g., GPT-4 Legal, BioMed-GPT) trained on domain corpuses.
  • It can enforce compliance rules (e.g., HIPAA, GDPR) and style guidelines (e.g., APA format, legal citation).
  • It can integrate with domain ontologies to resolve terminology ambiguity and maintain contextual consistency.
  • It can reduce human error and processing time by 40-70% compared to manual writing.
  • ...
• Examples:
  • Technical Documentation Systems, such as:
    • API Documentation Generators, such as:
      • REST API Documentation System for web service interface documentation.
      • APIDoc-Gen, auto-documenting software APIs using codebase analysis and user story inputs.
      • SDK Documentation Builder for development toolkit explanation.
    • Technical Manual Creators, such as:
      • Product User Guide Generator for consumer product instruction.
      • Equipment Maintenance Manual System for industrial equipment servicing.
  • Legal Document Systems, such as:
    • LegalDoc-AI, which drafts contract clauses using precedent databases and jurisdiction-specific regulations.
    • Contract Generation Systems, such as:
      • Commercial Agreement Builder for business transaction documentation.
      • Employment Contract Generator for personnel management standardization.
    • Compliance Documentation Tools, such as:
      • Regulatory Filing Generator for governmental reporting requirements.
      • Privacy Policy Creator for data protection compliance.
  • Medical Content Systems, such as:
    • Clinical Documentation Tools, such as:
      • Patient Care Protocol Generator for treatment standardization.
      • Medical Procedure Documentation System for healthcare process standardization.
      • ClinNote-AI for generating discharge summaries with ICD-11 code integration.
    • Patient Education Systems, such as:
      • Condition Information Generator for patient health literacy support.
      • Treatment Explanation Builder for medical intervention understanding.
  • ...
• Counter-Examples:
  • General-Purpose Text Editor, which lacks domain-specific knowledge integration.
  • Manual Documentation System, which lacks automated content generation capabilities.
  • Generic Content Management System, which lacks specialized domain rule enforcement.
  • Natural Language Generator, which lacks domain-specific terminology constraints.
  • Interactive Writing Assistant, which relies on continuous human input rather than domain automation.
• See: Automated Writing System, Domain-Specific Language, Knowledge Management System, Documentation Automation Tool, Content Generation System, Automated Writing Evaluation (AWE) System.

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References

2024a

• (Wang et al., 2024) ⇒ Tiannan Wang, Jiamin Chen, Qingrui Jia, Shuai Wang, Ruoyu Fang, et al. (2024). "Weaver: Foundation Models for Creative Writing". In: arXiv preprint arXiv:2401.17268.
  • QUOTE: This work introduces Weaver, our first family of large language models (LLMs) dedicated to content creation. Weaver is pre-trained on a carefully selected training corpus that focuses on improving the writing capability of large language models. We then fine-tune Weaver for creative writing and professional writing purposes and align it to the preference of professional writers using a suite of novel methods for instruction data synthesis and LLM alignment, making it able to produce more human-like texts and follow more diverse instructions for content creation. The Weaver family consists of models of Weaver Mini (1.8B), Weaver Base (6B), Weaver Pro (14B), and Weaver Ultra (34B) sizes, suitable for different applications and can be dynamically dispatched by a routing agent according to query complexity to balance response quality and computation cost. Evaluation on a carefully curated benchmark dataset for assessing the writing capability of LLMs shows Weaver models of all sizes outperform generalist LLMs several times larger than them. Notably, our most-capable Weaver Ultra model surpasses GPT-4, a state-of-the-art generalist LLM, on various writing scenarios, demonstrating the advantage of training specialized LLMs for writing purposes. Moreover, Weaver natively supports retrieval-augmented generation (RAG) and function calling (tool usage). We present various use cases of these abilities for improving AI-assisted writing systems, including integration of external knowledge bases, tools, or APIs, and providing personalized writing assistance. Furthermore, we discuss and summarize a guideline and best practices for pre-training and fine-tuning domain-specific LLMs.

2024b

• (Shi et al., 2024) ⇒ Yu-Zhe Shi, Haofei Hou, Zhangqian Bi, Fanxu Meng, Xiang Wei, Lecheng Ruan, and Qining Wang (2024). "AutoDSL: Automated Domain-Specific Language Design for Structural Representation of Procedures with Constraints". In: arXiv preprint arXiv:2406.12324.
  • QUOTE: Accurate representation of procedures in restricted scenarios, such as non-standardized scientific experiments, requires precise depiction of constraints. Unfortunately, Domain-specific Language (DSL), as an effective tool to express constraints structurally, often requires case-by-case hand-crafting, necessitating customized, labor-intensive efforts. To overcome this challenge, we introduce the AutoDSL framework to automate DSL-based constraint design across various domains. Utilizing domain specified experimental protocol corpora, AutoDSL optimizes syntactic constraints and abstracts semantic constraints. Quantitative and qualitative analyses of the DSLs designed by AutoDSL across five distinct domains highlight its potential as an auxiliary module for language models, aiming to improve procedural planning and execution.

2024c

• (Lee et al., 2024) ⇒ Minhwa Lee, Zae Myung Kim, Vivek Khetan, and Dongyeop Kang (2024). "Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants". In: arXiv preprint arXiv:2406.18675.
  • QUOTE: Large Language Models (LLMs) have assisted humans in several writing tasks, including text revision and story generation. However, their effectiveness in supporting domain-specific writing, particularly in business contexts, is relatively less explored. Our formative study with industry professionals revealed the limitations in current LLMs' understanding of the nuances in such domain-specific writing. To address this gap, we propose an approach of human-AI collaborative taxonomy development to perform as a guideline for domain-specific writing assistants. This method integrates iterative feedback from domain experts and multiple interactions between these experts and LLMs to refine the taxonomy. Through larger-scale experiments, we aim to validate this methodology and thus improve LLM-powered writing assistance, tailoring it to meet the unique requirements of different stakeholder needs.

2023a

• (Ding and Zou, 2023) ⇒ Linqian Ding and Di Zou (2023). "Automated Writing Evaluation Systems: A Systematic Review of Grammarly, Pigai, and Criterion with a Perspective on Future Directions in the Age of Generative Artificial Intelligence". In: Education and Information Technologies, 29, 14151–14203.
  • QUOTE: With the burgeoning popularity and swift advancements of automated writing evaluation (AWE) systems in language classrooms, scholarly and practical interest in this area has noticeably increased. This systematic review aims to comprehensively investigate current research on three prominent AWE systems: Grammarly, Pigai, and Criterion. Objectives include assessing each system’s characteristics, advantages, and drawbacks, analyzing prior studies’ frameworks, methodologyies, findings, and implications, and identifying research gaps and future directions. The analysis of 39 articles underscored an escalating interest in scrutinizing AWE systems, predominantly focusing on their efficacy and learner viewpoints. The findings demonstrated the positive impact of AWE systems on enhancing student writing proficiency, with both learners and educators conveying positive attitudes towards these digital tools. However, several noteworthy research gaps endure, including the need to further investigate the usage patterns of AWE tools, expanding the participants to wider language proficiency and research comparing AWE feedback with peer feedback. The majority of the studies focused on non-native English-speaking university students over a single academic semester, using quantitative research methods and mixed research methods. The review concludes by offering insights and recommendations for educators and researchers in the field, stressing the importance of tackling the identified research gaps and further delving into the potential of AWE systems in the age of generative artificial intelligence.

2023b

• (Huawei and Aryadoust, 2023) ⇒ Shulin Huawei and Vahid Aryadoust (2023). "A Systematic Review of Automated Writing Evaluation Systems". In: Education and Information Technologies, 28, 12345–12399.
  • QUOTE: Automated writing evaluation (AWE) systems are developed based on interdisciplinary research and technological advances such as natural language processing, computer sciences, and latent semantic analysis.

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