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.
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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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- The model names an outdated software version as the latest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
- 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