Kedis ku xidhan hagaha · Fudud Heerka

Taariikhaha Goynta Aqoonta ee Kediska LLMs

Wuxuu tijaabiyaa waxa goynta aqoontu tahay, sababta moodeeladu u qiimeeyaan taariikhda goynta iyaga u gaar ah, iyo sida goobidda, soo celinta iyo tariikhda duritaanka ay jawaabaha hadda u hayaan.

Wadooyinka haga ee la xidhiidhaTaariikhaha Goynta Aqoonta ee LLMs
Su'aal 1 ee 8

Waa maxay xadka aqoonta ee moodelka?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Waa maxay xadka aqoonta ee moodelka?
    • The date the model stops working
    • The approximate date after which its training data contains little or no information
    • The date the model was released to the public
    • The date the vendor ends customer support
  2. Maxay moodelku uusan mararka qaar hubin ama hoos u dhigaan xadka ay leeyihiin?
    • Vendors deliberately hide it from the model
    • Models delete their most recent data after training
    • Models count days incorrectly
    • The final months before the cutoff are thinly represented, because people keep writing about events long afterward
  3. Waa maxay habka ugu kalsoonida badan ee lagu ogaado xadka aqoonta moodeelka?
    • Check the vendor's published model documentation
    • Ask the model directly
    • Count the words in its answers
    • Look at the model's logo
  4. Waa maxay hagaajinta fudud ee saxda badan ee xaaladaha moodeelku u maleeyo taariikhda hadda jirta ee khaldan?
    • Fine-tuning the model every week
    • Turning the model off and on
    • Putting the current date in the system prompt
    • Using shorter prompts
  5. Chatbot leh awoodda daalacashada webka ayaa ka jawaabaya su'aal ku saabsan wararkii toddobaadkii hore. Keebaa sheegashada saxda ah?
    • Its training cutoff has moved to last week
    • It can no longer make factual errors
    • It retrains itself on every query
    • Its training cutoff is unchanged; it is reading new sources and is only as accurate as the pages it retrieves
  6. Why is retrieval usually preferred over fine-tuning for keeping facts current?
    • Fine-tuning is slower and more costly as a way to keep facts current
    • Fine-tuning is impossible for large models
    • Retrieval permanently changes the model's weights
    • Retrieval works without any documents
  7. Which of these is a typical sign of stale knowledge?
    • The model refuses every question
    • The model switches to another language
    • The model names an outdated software version as the latest
    • The model's answers are unusually short
  8. When building evals for cutoff-related problems, what should you include?
    • Only questions about events well before the cutoff
    • Questions whose correct answers changed after the cutoff
    • Questions with all dates removed
    • A question asking the model to state its cutoff