Categorical Organization in Memory: ChatGPT Organizes the 665 Topic Tags from My New Savanna Blog

I've just posted a new working paper. Title above, links, abstract, TOC, and two opening sections below:

Academia.edu: https://www.academia.edu/111354740/Categorical_Organization_in_Memory_ChatGPT_Organizes_the_665_Topic_Tags_from_My_New_Savanna_Blog
SSRN: https://ssrn.com/abstract=4663978
ReserchGate: https://www.researchgate.net/publication/376481426_Categorical_Organization_in_Memory_ChatGPT_Organizes_the_665_Topic_Tags_from_My_New_Savanna_Blog

Abstract: I gave ChatGPT three lists of topics for which it had to propose categories into which the topics could be sorted. Two lists were relatively short, 56 and 53 topics; I asked ChatGPT to propose six organizing categories for each. One list was much longer, 655 topics; I asked ChatGPT to propose 12 for categories for it. In all cases the proposed categories were reasonable. ChatGPT explained each proposed category either with a pair of sentences (the short lists), or with characterizing phrases (the long list). These characterizations were reasonable. In a further task, when asked to place topics under the proposed categories, ChatGPT placed many topics under the first two categories and very few under the last two. Though quite different in detail, this task has a rough formal similarity to generating a coherent story that is organized on three levels: 1) the whole story, 2) story segments, 3) sentences in story segments.

Contents

Organizing lists of categories into a coherent structure 1
The categories ChatGPT proposed 3
Formal similarity with story generation 5
What happens when ChatGPT lists tags under each category? 8
How would I have approached these tasks myself? 10
Propose categories to sort a short “top-level” list of 56 topics 10
Propose categories to sort a short arbitrary sub-list of 53 topics 14
Propose categories to sort the full list of 665 topics 16
Sort a short list and place the topics under the appropriate category 21    

Organizing lists of categories into a coherent structure

Some months ago I decided to see how ChatGPT would react to some associative clusters I had made. Associative cluster? Simple, a list of words I created by free association on some particular topic, for example:

atoms, periodic table, bonds, compounds, elements, molecules, reaction, oxidation, acids and bases, reagents, alchemy, changing liquids from one color to another, distilling, condenser, precipitate

I would present such a cluster to Chatster and see how it would respond. In that case, it informed me, “These are all concepts in the field of chemistry,” which is true. It then went on to tell me something about those fields.

This time I decided to tease it with a different list, the category tags for my New Savanna blog, which currently contains 655 items. In prompting ChatGPT with this list didn’t have anything in particular in mind; I just wanted to see what happened. It seemed, however, a bit extreme to dump the whole 655 item list on ChatGPT. So I started with a sub-set, two different subsets in fact. THEN, I gave it the whole list. What happened turned out to be interesting, as is sometimes the case.

In the case of the sub-sets, after a bit of interaction, I asked ChatGPT to organize the whole list in into a half-dozen categories, which it did. I asked it to organize the whole list, all 655 categories, into a dozen categories. No problem. Why is this interesting?

Sorting lists

First of all, note that we’re talking about organizing lists. This is a classic and fundamental problem in computing. Put things in alphabetical order, numerical order, in order by size, by zip code, by weight, date of birth, and so forth. Programs do this all the time. Given a criterion by which to establish a list, we know how to do this computationally.

What makes this particular problem interesting is the organizational criterion: inherent conceptual structure. How do you state conceptual structure in computational terms? It is no exaggeration to say that students of database design, artificial intelligence, and computational linguistics have devoted a great deal of effort to that problem.

We can thus see that sorting lists has two aspects: 1) specifying the sort criterion, and 2) applying it to the list. These are different kinds of problem. The first is about what things are and the second is about moving them around. There is an analog to this in linguistics. The first is about paradigmatic structure, and the second is about syntagmatic structure. To borrow terms from the great Russian linguist, Roman Jakobson, the first involves the axis of selection and the second is about the axis of combination.