Replication Data for: 'Out of One, Many: Using Language Models to Simulate Human Samples'
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'We propose and explore the possibility that language models can be studied as effective proxies for specific human sub-populations in social science research. Practical and research applications of artificial intelligence tools have sometimes been limited by problematic biases (such as racism or sexism), which are often treated as uniform properties of the models. We show that the “algorithmic bias” within one such tool-- the GPT-3 language model-- is instead both fine-grained and demographically correlated, meaning that proper conditioning will cause it to accurately emulate response distributions from a wide variety of human subgroups.  We term this property algorithmic fidelity and explore its extent in GPT-3. We create 'silicon samples' by conditioning the model on thousands of socio-demographic backstories from real human participa' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["artificial intelligence","computational social science","machine learning","public opinion"]} <|eot_id|>
 Here is the Description of a dataset. Description:'We propose and explore the possibility that language models can be studied as effective proxies for specific human sub-populations in social science research. Practical and research applications of artificial intelligence tools have sometimes been limited by problematic biases (such as racism or sexism), which are often treated as uniform properties of the models. We show that the “algorithmic bias” within one such tool-- the GPT-3 language model-- is instead both fine-grained and demographically correlated, meaning that proper conditioning will cause it to accurately emulate response distributions from a wide variety of human subgroups.  We term this property algorithmic fidelity and explore its extent in GPT-3. We create 'silicon samples' by conditioning the model on thousands of socio-demographic backstories from real human participa' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
We propose and explore the possibility that language models can be studied as effective proxies for specific human sub-populations in social science research. Practical and research applications of artificial intelligence tools have sometimes been limited by problematic biases (such as racism or sexism), which are often treated as uniform properties of the models. We show that the “algorithmic bias” within one such tool-- the GPT-3 language model-- is instead both fine-grained and demographically correlated, meaning that proper conditioning will cause it to accurately emulate response distributions from a wide variety of human subgroups.  We term this property algorithmic fidelity and explore its extent in GPT-3. We create 'silicon samples' by conditioning the model on thousands of socio-demographic backstories from real human participa
artificial intelligence; computational social science; machine learning; public opinion