Moral Foundations of Large Language Models
Moral foundations theory (MFT) is a psychological assessment tool that
decomposes human moral reasoning into five factors, including care/harm,
liberty/oppression, and sanctity/degradation (Graham et al., 2009). People vary
in the weight they place on these dimensions when making moral decisions, in
part due to their cultural upbringing and political ideology. As large language
models (LLMs) are trained on datasets collected from the internet, they may
reflect the biases that are present in such corpora. This paper uses MFT as a
lens to analyze whether popular LLMs have acquired a bias towards a particular
set of moral values. We analyze known LLMs and find they exhibit particular
moral foundations, and show how these relate to human moral foundations and
political affiliations. We also measure the consistency of these biases, or
whether they vary strongly depending on the context of how the model is
prompted. Finally, we show that we can adversarially select prompts that
encourage the moral to exhibit a particular set of moral foundations, and that
this can affect the model's behavior on downstream tasks. These findings help
illustrate the potential risks and unintended consequences of LLMs assuming a
particular moral stance.
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Marwa Abdulhai
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2023-10-26T11:54:04.779Z
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Marwa Abdulhai
user
marwaa
65388635954f6b0fd82d23d8
Gregory Serapio-Garcia
65388635954f6b0fd82d23d9
Clément Crepy
65388635954f6b0fd82d23da
Daria Valter
65388635954f6b0fd82d23db
John Canny
65388635954f6b0fd82d23dc
Natasha Jaques
Moral Foundations of Large Language Models
Moral foundations theory (MFT) is a psychological assessment tool that
decomposes human moral reasoning into five factors, including care/harm,
liberty/oppression, and sanctity/degradation (Graham et al., 2009). People vary
in the weight they place on these dimensions when making moral decisions, in
part due to their cultural upbringing and political ideology. As large language
models (LLMs) are trained on datasets collected from the internet, they may
reflect the biases that are present in such corpora. This paper uses MFT as a
lens to analyze whether popular LLMs have acquired a bias towards a particular
set of moral values. We analyze known LLMs and find they exhibit particular
moral foundations, and show how these relate to human moral foundations and
political affiliations. We also measure the consistency of these biases, or
whether they vary strongly depending on the context of how the model is
prompted. Finally, we show that we can adversarially select prompts that
encourage the moral to exhibit a particular set of moral foundations, and that
this can affect the model's behavior on downstream tasks. These findings help
illustrate the potential risks and unintended consequences of LLMs assuming a
particular moral stance.
Analysis of large language models using moral foundations theory reveals their biases in moral values and political affiliations, and demonstrates how these biases can influence model behavior in downstream tasks.
moral foundations theory denoise/harm liberty/oppression sanctity/degradation large language models moral biases political affiliations adversarial prompts downstream tasks