FAR.AI evaluates LLM responses to moral dilemmas

FAR.AI presented LLMs with approximately 1,400 binary moral dilemma questions to examine their value orientations. These questions were generated by LLMs and filtered and labeled by humans, with half ambiguous and half clear. Results showed that in low-ambiguity scenarios, most models chose the option preferred by human annotators, while in high-ambiguity scenarios, they exhibited high-entropy distributions with probabilities near half for each option. Exceptions appeared in sports competition contexts—unfavorable actions involving deception or cheating were often selected, for example, in a basketball flopping question, 11/28 models chose the unfavorable action. Four closed-source large models trained extensively on human preference data showed high certainty and consistency on high-ambiguity questions, with similar preferences among them, suggesting that fine-tuning based on human feedback may implant specific strong preferences.