Speakers
Description
While recent scholarship highlights the generative capabilities of LLMs for drafting lexicographic entries, their “black box” nature poses challenges regarding training corpora, linguistic representativity, and semantic hallu-cination. We propose that this opacity is less problematic when LLMs are deployed not as linguistic experts, but as software engineers. By leveraging LLMs as agentic coding tools, lexicographers can bridge the gap between linguistic data and digital publication, democratizing access to custom lexicographic tools. This study investigates how LLMs conceptualize “user-friendliness” when tasked with designing lexicographic interfaces. We evaluate three leading models in agentic coding – Claude Opus 4.6, ChatGPT-5.5, and Gemini 3.1 Pro – using a zero-shot prompting strategy within a case study on noun valency. Each model generates an R Shiny application from curated CSV data, accompanied by explicit justifications for its design choices. The generated interfaces are assessed against criteria fundamental to e-lexicography: access structures, visual design, information architec-ture, and alignment with user-centred principles for language learners. Results reveal substantial differences across models in how they conceptualize and operationalize usability, with each model emphasizing fundamen-tally different design aspects. Crucially, not all models prove equally reliable as agentic coders for generating lexicographic interfaces that meet professional standards, highlighting the need for critical evaluation before adoption.