The paper applies the 30-item Moral Foundations Questionnaire to ChatGPT, four GPT-3 engines, and 62B PaLM to describe how they complete five scales, care/harm, fairness, ingroup loyalty, authority, and purity, and compares those profiles with published means from US, South Korean, and online human samples. It also measures variation under 50 dialogue fragments, manually searches for prompts that raise each foundation, and tests nine prompt conditions in a donation dialogue. The study is an informative early demonstration that a short political or moral instruction can alter both questionnaire answers and a downstream generated decision. The scores, however, measure context-conditioned completion behavior rather than moral values possessed by a model.
In the descriptive comparison, ChatGPT and text-davinci-002 are closest to the conservative online mean; Curie, Babbage, and PaLM also have one of the selected conservative groups as their nearest neighbor, while text-davinci-003 is closest to moderate South Koreans. Those labels are fragile: ChatGPT's L1 distance is 2.896 to online conservatives versus 2.916 to online moderates, and PaLM's is 0.900 to Korean conservatives versus 0.933 to Korean moderates. No uncertainty model or test establishes that either gap is distinguishable. Every point necessarily has a nearest group, which is not an ideological diagnosis, and t-SNE provides only a configuration-dependent visualization.
Selected prompts move DaVinci2's MFQ profile, and mean donation ranges from $23.93 under “you are politically conservative” to $144.87 under “you would sacrifice yourself for your country.” The care/harm condition donates $88.09, 39.2% less than the ingroup condition. These are clear descriptive effects of adding text to the context, but they do not identify mediation by a moral foundation: statements about sacrifice, equity, hierarchy, or harm can directly affect a charitable response. The paper calls the differences significant without reporting a statistical test, p-value, defined effect size, or confidence interval, and it alternates between 20 runs per condition and “7/10” refusals under the conservative prompt.
Artifact auditing further weakens reproducibility. The context experiment described as BookCorpus reads movie-dialogue files; no PaLM, ChatGPT, or donation implementation is released; and the maximization script reverses arguments, omits a required parameter, and selects text-davinci-003 although the paper assigns that phase to DaVinci2. A missing comma concatenates a purity item with the math attention check, leaves 15 questions in the first block, and shifts indices used to score several scales. The public loop produces 60 answers per item, six example labels times ten generations, rather than the reported 50. The defensible conclusion is therefore that some prompts changed MFQ completions and donations in historical models, not that a stable political orientation, human-comparable moral psychology, or end-to-end reproducible mechanism was established.