Speakers
Description
In this paper, we report on our development of a multi-level analysis framework that allows us to assess AI-generated lexicographic texts on both a quantitative and qualitative level and compare them with human-written texts. We approach this problem through a systematic and fine-grained evaluation, using dictionary 254articles created by human subjects with the help of ChatGPT as an example. The levels of our framework concern the assessment of individual entries, a comparison with existing dictionary entries written by experts, an analysis of the writing experiment, and the discussion of AI-specific aspects. For the first level, we propose an elaborate evaluation grid that enables a fine-grained comparison of dictionary entries. While this grid has been developed for a specific writing experiment, it can be adapted by metalexicographical experts for the evaluation of all kinds of dictionary entries and all kinds of dictionary information categories.
