A very brief, somewhat technical take on Large Language Models (LLMs). They are *effectively* analogs of Bloom filters for searching vector databases using natural language interfaces, and reporting back using natural language.
This statement gives you all you need to know about potential and pitfalls, as long as you abstract the keywords to their essentials ....
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Vector Database = encodes information using more or less artificial, possibly statistical descriptions of recorded facts about the objects in the database.
Natural language = imprecise language ie cannot have formal proofs about internal consistency or ambiguity of the query or the query result.
Bloom filter = sensitive but not specific way to search vector databases. Designed to not have false negatives (i.e. not miss), but will generate false hits (akin to hallucinations).
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There are also technical issues that make LLMs dissimilar to Bloom filters, but they win on using natural language as a query language; it frees people from formulating a very complex query in a formal language, reducing the barrier of asking questions for non-experts in a field. Furthermore, the answer is also not formulated in a formal language or served in a technical manner, even further reducing the barriers in interpreting answers.
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A society of engineers would acknowledge this limitation and use LLMs as accelerants the way we use high temperature settings in simulated annealing (SA) global optimization schemes: as a quick way to generate an approximate answer, and then painstakingly (the "cooling scheme" in SA) refine the answer by making it more precise.
Unfortunately, we are not a society of engineers, but a culture of Dunning Kruger susceptibles. Enjoy your dopamine fix from the slop now.
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@ChristosArgyrop a society of librarians also. Of course the mission of the library profession includes teaching all consumers of information the how and the why of evaluating information for relevance, authority, currency, permanence - quality. At which mission I and the rest of my profession have demonstrably failed. 😢😭🤬
@ChristosArgyrop if you are average dunning krugerrand is less of an issue
@ChristosArgyrop #DKsusceptibles, I like the sound of that. 👍
Thanks for the details and definitions.
