Please examine the text below carefully and list words or expressions which may be difficult to translate, but when writing the list, do not capitalize any words or expressions which don't require capitalization.
is too long, and only the part marked red is executed correctly, but this follow-up prompt will fix the capitalization in the list:
Please re-examine that text and this time when writing the list, do not capitalize any words or expressions which do not require capitalization.
Further tests involved suggesting translations for the expressions, with or without a translated text and building tables with example sentences:
What about the quality of the selections? Well, I used memoQ's term extraction module on the same text I submitted to ChatGPT for term extraction in order to compare something with which I am quite familiar with this new process.
memoQ identified a few terms based on frequency, which ChatGPT ignored, but these were arguably terms that a qualified specialist would have known anyway. And ChatGPT did a superior job of selecting multi-word expressions with no "noise". It also selected some very relevant single-occurrence phrases which might be expected to arise more in later, similar texts.
|Split screen review of memoQ extraction vs. ChatGPT results
The split-screenshot is an intermediate result from one of my many tests. The overlayed red box was intended to show a conversation partner the limits of ChatGPT's "alphabetizing skill", and the capitalization of the German is not correct after a prompt to correct the capitalization of adjectives misfired. It is not always trivial to get formatting exactly as I want it. However, looking at the results of each program side-by-side like this showed me that ChatGPT had in fact identified the nearly all the most relevant single words and phrases in my text. And for other texts with dates or citation formats, these were also collected by ChatGPT as "relevant terms", giving me an indication of what legislation I might want to use as reference documents and what auto-translation rules might also be helpful.
I also found that the split view as above helped me to work my way through the noise in the memoQ term candidate list much faster and make decisions about which terms to accept. The terms of interest found in memoQ but not selected by ChatGPT were few enough that I am not at all tempted to suggest people follow my traditional approach with the memoQ term extraction module and skip the work with ChatGPT.
My preferred approach would be to do a quick screening in ChatGPT, import the results into a provisional (?) term base and then, as time permits, use that resource in a memoQ term extraction to populate the target fields in the extraction grid. With those populated terms in place, I think the review of the remaining candidates would proceed much more efficiently.
All in all, I found Uwe's book to be a useful reference for teaching and for my personal work; it is one of the few texts I have seen on LLM use which is sober and modest enough in its claims that I was inspired to test them. The sale price is also well within anyone's means: about $10 for the e-book and $16 for the paperback on Amazon. For the "term curious" without access to professional grade tools, it's a great place to get started building better glossaries and for more seasoned wordworkers it offers interesting, probably useful suggestions.
The book is available HERE from Amazon.