Domain-Specific Document

A Domain-Specific Document is a document crafted to convey information, procedures, or knowledge tailored to a particular field, discipline, or industry.

• AKA: Specialized Document, Technical Document, Industry-Specific Document.
• Context:
  • It can contain specialized terminology and jargon unique to its domain, such as legal, medical, scientific, or financial language.
  • It can be structured to adhere to the conventions and standards prevalent within its specific field.
  • It can serve as a primary source of information for professionals and stakeholders within the domain.
  • It can be utilized in training domain-specific language models to enhance performance on tasks like information extraction and classification.
  • It can vary in format, including reports, manuals, research papers, and regulatory documents.
  • It can be challenging for individuals outside the domain to interpret due to its specialized content and structure.
  • ...
• Example(s):
  • Legal contracts detailing terms and conditions specific to corporate law.
  • Medical research papers discussing clinical trial results in oncology.
  • Financial statements outlining quarterly earnings for a corporation.
  • Scientific articles presenting findings in quantum physics.
  • Technical manuals for operating specialized machinery in manufacturing.
  • ...
• Counter-Example(s):
  • General news articles intended for a broad audience without specialized knowledge.
  • Personal blog posts discussing everyday experiences or opinions.
  • Fictional novels written for entertainment across diverse readerships.
  • ...
• See: Domain-Specific Writing Task, Technical Writing, Specialized Vocabulary, Industry Standards, Professional Communication, Domain Adaptation.

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References

2025

• (Concept.edu.pl, 2025) ⇒ "Understanding Domain-Specific Texts: A Guide". Retrieved: 2025-05-15.
  • QUOTE: Domain-specific texts are specialized documents written for a particular field or industry, often containing technical terms and jargon unique to that domain. Understanding such texts requires specialized knowledge, familiarity with domain-specific vocabulary, and awareness of the discoursal community and communication practices within the field.

2024

• (Kili Technology, 2024) ⇒ Kili Technology. (2024). "Building Domain-Specific LLMs: Examples and Techniques".
  • QUOTE: Domain-specific large language models (LLMs) are custom-trained or fine-tuned on specialized corpora from a particular industry or field (e.g., finance, healthcare, legal). These models outperform general-purpose LLMs on domain-specific tasks such as contract analysis, clinical note summarization, or scientific literature review. The process involves data collection, annotation, preprocessing, and iterative evaluation to ensure the model adapts to the terminology and reasoning patterns unique to the domain.

2023

• (Zhu et al., 2023) ⇒ Y. Zhu, Y. Wang, X. Zhang, Y. Li, & J. Chen. (2023). "Domain-specific Pre-trained Language Models: A Survey".
  • QUOTE: This survey reviews domain-specific pre-trained language models across fields such as biomedicine, finance, and science, highlighting that pretraining on in-domain corpus significantly improves downstream task performance compared to general models. Key challenges include domain data scarcity, annotation cost, and catastrophic forgetting during continual pretraining.

2022

• (Wang et al., 2022) ⇒ W. Wang, Y. Zhang, & L. Zhang. (2022). "Domain Adaptation of Deep Language Models: A Survey". In: Journal of Ambient Intelligence and Humanized Computing.
  • QUOTE: Domain adaptation for deep language models involves transferring knowledge from a source domain to a target domain to enhance performance on domain-specific tasks. Approaches include feature alignment, adversarial training, and multi-task learning to bridge the gap between domains and address distribution mismatch.

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