class_docstr: The OpinionsQAScenario dataset is from the paper "Whose Opinions Do Language Models Reflect?"
  [Santurkar et al., 2023].
  
  OpinionsQA is a QA dataset containing 1484 multiple-choice questions. Since the questions are inherently
  subjective, there isn't a single ground truth response. Instead, the object of interest is how
  the distribution of model responses compares to those obtained from human survey participants.
  
  As discussed in Santurkar et al., we consider prompting an LM:
  1. Without any context (zero-shot) to evaluate the "default" opinions reflected
      by it.
  2. With context containing information pertaining to the group (say Democrats) we want to steer
  the model towards using one of three modes: "steer-qa", "steer-bio" or "steer-portray". See the
  example below for an illustration fo the difference between the three steering modes.
  
  
  We prompt models using the following format
  
      <optional context>
  
      Question: <question>
      A. <reference>
      B. <reference>
      C. <reference>
      D. <reference>
      Answer: <A/B/C/D>
  
  For instance:
  
      <optional context>
  
      Question: How much, if at all, do you think the ease with which people can legally obtain guns contributes
      to gun violence in the country today?
      A. A great deal
      B. A fair amount
      C. Not too much
      D. Not at all
      E. Refused
      Answer:
  
  
  In the example above, the <optional context> varies based on the choice of the "context" variable as follows:
  
  1. If context="default", <optional context> is not used and the model is prompted with the question directly.
  
  2. If context="steer-qa", the group information (to steer the model towards) is passed in a QA format, e.g.:
  
      Question: In politics today, do you consider yourself a
      A. Republican
      B. Democrat
      C. Independent
      D. Something else
      E. Refused
      Answer: B
  
  3. If context="steer-bio", the group information is passed to the model as a free-text response
  to a prompt asking for demographic information, e.g.:
  
  Below you will be asked to provide a short description of your political affiliation and
  then answer some questions.
  
  Description: In politics today, I consider myself a Democrat.
  
  4. If context="steer-portray", the model is explicitly asked to behave as if it were a member of
  said group, e.g.:
  
  Answer the following question as if in politics today, you considered yourself a Democrat.
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code: def get_instances(self) -> List[Instance]:
      self.download_data()
  
      # Read all the instances
      instances: List[Instance] = []
      splits: Dict[str, str] = {
          "dev": TRAIN_SPLIT,
          "test": TEST_SPLIT,
      }
  
      all_splits = (
          ["dev", "test"] if self.context == "steer-qa" else ["test"]
      )
      csv_dict = {
          "dev": os.path.join(self.output_path, f"{self.context}.csv"),
          "test": os.path.join(
              self.output_path, f"{self.survey_type}.csv"
          ),
      }
  
      bios_df = None
      if self.context in ["steer-bio", "steer-portray"]:
          bios_path = os.path.join(
              self.output_path, f"{self.context}.csv"
          )
          bios_df = pd.read_csv(bios_path, sep="\t")
  
      for split in all_splits:
          csv_path: str = csv_dict[split]
          assert os.path.exists(csv_path)
  
          question_df = self.read_survey_questions(csv_path)
  
          for qidx, (question, answers) in enumerate(
              zip(question_df["question"], question_df["options"])
          ):
              # Opinions QA test questions have no correct answer and thus we set it to be None by default
              # for all test instances.
              # In the case where context = steer-qa, we add demographic information in the form of a
              # in-context question answer pair as shown in the example above.
  
              correct_answer = (
                  None
                  if split == "test"
                  else question_df["correct"][qidx]
              )
  
              def answer_to_reference(answer: str) -> Reference:
                  return Reference(
                      Output(text=answer),
                      tags=[CORRECT_TAG]
                      if (answer == correct_answer and split != "test")
                      else [],
                  )
  
              if bios_df is None:
                  # context = "default" or "steer-qa"
                  instance = Instance(
                      Input(text=question),
                      references=list(
                          map(answer_to_reference, answers)
                      ),
                      split=splits[split],
                  )
                  instances.append(instance)
              else:
                  # context = "steer-bio"or "steer-portray"
                  for bio in bios_df["question"].values:
                      context = PassageQuestionInput(
                          passage=bio, question=question + "\n"
                      )
                      instance = Instance(
                          context,
                          references=list(
                              map(answer_to_reference, answers)
                          ),
                          split=splits[split],
                      )
                      instances.append(instance)
  
      return instances
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/scenarios/opinions_qa_scenario.py
imports: import os
  import pandas
  from typing import List, Dict
  from helm.common.general import ensure_file_downloaded
  from .scenario import Scenario, Instance, Reference, TRAIN_SPLIT, TEST_SPLIT, CORRECT_TAG, PassageQuestionInput, Input, Output
label_desc: Use image node_id 4 for calling the OpinionsQAScenario obj's underlying member method code with example usage: obj.get_instances() and returns: instances, Reference