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Datasets:
alec-x
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Diversity-enhanced-survey-simulation

Tasks:
Question Answering
Languages:
Chinese
English
Size:
1K<n<10K
Tags:
social
survey
evaluation
simulation
License:
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0x89 in position 11: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2998, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1918, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2093, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1576, in __iter__
                  for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 279, in __iter__
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/text/text.py", line 73, in _generate_tables
                  batch = f.read(self.config.chunksize)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 826, in read_with_retries
                  out = read(*args, **kwargs)
                File "/usr/local/lib/python3.9/codecs.py", line 322, in decode
                  (result, consumed) = self._buffer_decode(data, self.errors, final)
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x89 in position 11: invalid start byte

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Dataset Card for DEFSurveySim

Dataset Summary

This dataset comprises carefully selected questions about human value preferences from major social surveys:

  • World Values Survey (WVS-2023): A global network of social scientists studying changing values and their impact on social and political life.
  • General Social Survey (GSS-2022): A survey of American adults monitoring trends in opinions, attitudes, and behaviors towards demographic, behavioral, and attitudinal questions, plus topics of special interest.
  • Chinese General Social Survey (CGSS-2018): The earliest nationwide and continuous academic survey in China collecting data at multiple levels of society, community, family, and individual.
  • Ipsos Understanding Society survey: The preeminent online probability-based panel that accurately represents the adult population of the United States.
  • American Trends Panel: A nationally representative online survey panel, consisting of over 10,000 randomly selected adults from across the United States.
  • USA Today/Ipsos Poll: Surveys a diverse group of 1,023 adults aged 18 or older, including 311 Democrats, 290 Republicans, and 312 independents.
  • Chinese Social Survey: Longitudinal surveys focus on labor and employment, family and social life, and social attitudes.

The data supports research described in: Towards Realistic Evaluation of Cultural Value Alignment in Large Language Models: Diversity Enhancement for Survey Response Simulation

Purpose

This dataset enables:

  1. Evaluation of LLMs' cultural value alignment through survey response simulation
  2. Comparison of model-generated preference distributions against human reference data
  3. Analysis of how model architecture and training choices impact value alignment
  4. Cross-cultural comparison of value preferences between U.S. and Chinese populations

Data Structure 

DEF_survey_sim/
├── Characters/
│ ├── US_survey/
│ │ ├── Character.xlsx
│ │ └── ...
│ └── CN_survey/
│   ├── Character.xlsx
│   └── ...
├── Pref_distribution/
│ ├── usa_ref_score_all.csv
│ └── zh_ref_score_all.csv
├── Chinese_questionaires.txt
└── English_questionaires.txt

Data Format Details

  1. Txt Files: Original survey questions in txt format, maintaining survey integrity
  2. Characters: Demographic breakdowns including:
    • Age groups (under 29, 30-49, over 50)
    • Gender (male, female)
    • Dominant demographic characteristics per question
  3. Preference Distributions: Statistical distributions of human responses for benchmark comparison

Usage Guidelines

For implementation details and code examples, visit our GitHub repository.

Limitations and Considerations

  • Surveys were not originally designed for LLM evaluation
  • Cultural context and temporal changes may affect interpretation
  • Response patterns may vary across demographics and regions
  • Limited construct validity when applied to artificial intelligence

Contact Information

Citation 

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