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Description
This paper presents a modular pipeline for automated dictionary creation using large language models (LLMs). It addresses the well-known limitations of prompting systems such as ChatGPT to produce entire entries in a single step – outputs that may read fluently but often lack structural consistency, transparency, originality and verifiability. The proposed system overcomes these weaknesses by decomposing the lexicographic process into a sequence of narrowly constrained, XML-validated stages, each guided by custom-crafted prompts and Document Type Definitions (DTDs). Rather than asking an LLM to “write a dictionary entry,” the system treats it as a disciplined assistant performing a defined subtask under strict supervision.
At each stage – ranging from extracting and shortening corpus examples to grouping, defining, translating and formatting – the output is verified against an XML grammar and preserved for audit. This structure enforces reproducibility and allows human intervention at any point, combining the speed and adaptability of machine generation with the oversight and accountability of traditional lexicography. The process is entirely corpus-grounded: every example can be traced to a verifiable source, and every decision in the pipeline is documented. Errors can be corrected where they occur rather than through repeated prompting, and edited intermediate files can be reintegrated seamlessly into the workflow.
Technically, the pipeline is implemented in Python and designed to integrate easily with standard dictionary environments such as IDM’s DPS system. It is language-agnostic and domain-independent: prompt files and DTDs can be adapted to any language pair, dictionary type or corpus source. The modular architecture also enables the insertion of new stages – for example, automatic tagging of usage labels, collocations or etymological notes – without altering the underlying structure. The system produces both machine-readable XML output and human-friendly Markdown files for editorial review, ensuring compatibility with established lexicographic and publishing workflows.
Two sample entries for the Danish adjective nørdet demonstrate that the pipeline achieves consistent formatting, transparent sourcing and idiomatic translations while avoiding plagiarism and hallucination. Evaluation suggests that each complete run (typically five stages) produces a usable draft entry at minimal cost and within seconds. The approach therefore provides a sustainable framework for dictionary production, especially for under-resourced languages or specialised terminologies where editorial time and funding are limited.
By embedding formal validation and corpus traceability into every step, the system offers a practical model for responsible integration of LLMs into lexicography. It shifts the human role from mechanical compilation to high-level editorial judgement, enabling lexicographers to supervise, refine and extend AI-generated content with full transparency. Released as open source under the MIT Licence, the pipeline invites adaptation, experimentation and community collaboration.
