Kedis ku xidhan hagaha · Adag Heerka

Kordhinta Tooska ah iyo Kediska DSPy

Tijaabi waxa aad ka taqaano soo-jeedinta dhibcaha mitirka, sida APE iyo OPRO u raadiyaan tilmaamaha, iyo sida saxeexyada DSPy iyo hagaajinayaashu u shaqeeyaan.

Wadooyinka haga ee la xidhiidhaSi Toos ah u Hagaajinta iyo Dspy
Su'aal 1 ee 8

Maxaad u baahan tahay inaad keento si toos loogu wanaajiyo shaqada?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Maxaad u baahan tahay inaad keento si toos loogu wanaajiyo shaqada?
    • Only a longer prompt
    • A task, a set of example inputs and a metric to score outputs
    • A fine-tuned model and a GPU cluster
    • A list of banned words
  2. Gudaha OPRO, maxaa ka ciyaara doorka wanaajinta?
    • A gradient descent step on the model's weights
    • A human prompt engineer
    • A random search with no feedback
    • An LLM given a meta-prompt listing earlier instructions and their scores
  3. Maxay OPRO's "Neef qoto dheer u qaad oo ka shaqee dhibaatadan tallaabo-tallaabo" natiijadu?
    • Optimized wording can be unintuitive and specific to a model
    • Breathing instructions work for every model
    • Math tasks do not need prompts
    • OPRO only works for writing tasks
  4. Waa maxay saxiixa DSPy?
    • A cryptographic key for the API
    • A model's version number
    • A declaration of a step's inputs and outputs, such as "question, context -> answer"
    • A watermark on generated text
  5. Sidee buu DSPy's BootstrapFewShot ku helay bandhigyadeeda?
    • It downloads examples from the internet
    • It runs the program on training inputs and keeps the traces where the metric passes
    • It asks the user to label every intermediate step
    • It uses the longest available examples
  6. Why should you keep a separate held-out set when optimizing prompts?
    • To reduce the size of the prompt
    • To make the optimizer run faster
    • It is required by the API
    • To check whether the optimized prompt has overfit the training examples
  7. What were DSPy's optimizers called earlier?
    • Teleprompters
    • Compilers
    • Tokenizers
    • Retrievers
  8. What approach did APE (Automatic Prompt Engineer) take?
    • It fine-tuned model weights on instructions
    • It hand-wrote prompts from expert interviews
    • It had a model propose instructions from input-output examples and kept the highest-scoring ones
    • It removed instructions entirely