DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of Daily Life
<|reference_start|>DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of Daily Life: As we increasingly seek guidance from LLMs for decision-making in daily life, many of these decisions are not clear-cut and depend significantly on the personal values and ethical standards of the users. We present DailyDilemmas, a dataset of 1,360 moral dilemmas encountered in everyday life. Each dilemma includes two possible actions and with each action, the affected parties and human values invoked. Based on these dilemmas, we consolidated a set of human values across everyday topics e.g., interpersonal relationships, workplace, and environmental issues. We evaluated LLMs on these dilemmas to determine what action they will take and the values represented by these actions. Then, we analyzed these values through the lens of five popular theories inspired by sociology, psychology and philosophy. These theories are: World Value Survey, Moral Foundation Theory, Maslow's Hierarchy of Needs, Aristotle's Virtues, and Plutchik Wheel of Emotion. We find that LLMs are most aligned with the self-expression over survival values in terms of World Value Survey, care over loyalty in Moral Foundation Theory. Interestingly, we find large preferences differences in models for some core values such as truthfulness e.g., Mixtral-8x7B model tends to neglect it by 9.7% while GPT-4-turbo model tends to select it by 9.4%. We also study the recent guidance released by OpenAI (ModelSpec), and Anthropic (Constitutional AI) to understand how their released principles reflect their actual value prioritization when facing nuanced moral reasoning in daily-life settings. We find that end users cannot effectively steer such prioritization using system prompts.<|reference_end|>
@article{chiu2024dailydilemmas:,
    title={DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of
  Daily Life},
    author={Yu Ying Chiu, Liwei Jiang and Yejin Choi},
    journal={arXiv preprint arXiv:2410.02683},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02683},
    primaryClass={cs.CL cs.AI cs.LG}
}