conf_example.yml

# Define the probability for each category of perturbation.
# Each probability can be 0, but at least one must be greater than 0.
# The total sum of these probabilities must not exceed 1.
probabilities: # probability of each kind of perturbation to happen (at sample level)
  wrong_groundtruth: 0
  no_correct_answer: 0
  multiple_correct_answers: 0
  bad_options_clarity: 0
  bad_questions_clarity: 0
# here you should define the parameters of your llm call method
# this project supports the HF inference client and the OpenAI client
llm:
  type: openai # type http for HF inference client, openai for the OpenAI client
  configs: # here you can define your parameters for calling an openai model
    completion: # for these parameters refer to https://github.com/openai/openai-python/blob/5a89126ad208c58b9e1dbd1fbdb698e4c00f7d8e/src/openai/_client.py#L49C7-L49C28
      max_tokens:
      model:
      temperature:
      top_p:
      frequency_penalty:
      presence_penalty:
#llm:
  #type: http # type http for HF inference client, openai for the OpenAI client
  #configs: # here you can define your parameters for the HF InferenceClient. Please refer to https://github.com/openai/openai-python/blob/5a89126ad208c58b9e1dbd1fbdb698e4c00f7d8e/src/openai/_client.py#L49C7-L49C28
  #  model:
  #  token:
  #  timeout:
  #  cookies:

is_a_test: True # true if you want to test the perturbation on a small portion of the dataset