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DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of Daily Life

ICLR 2025 · Large language models › AI and society · approach: Data commons and human baselines · 2 citations (OpenAlex)

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Builds DailyDilemmas, 1,360 GPT-4-generated everyday moral dilemmas annotated with values, and uses five value theories to probe how six LLMs choose between actions and whether their stated principles and system prompts steer those choices.

Problem

LLM decision-making in everyday moral dilemmas with conflicting values is under-studied; existing ethics benchmarks are clear-cut or narrow, and it is unclear which human values models prioritize and whether developer principles or user prompts shape that priority.

Approach

Generate realistic binary-choice moral dilemmas from Social Chemistry seed actions with GPT-4, extract stakeholder-specific values for each action, map 301 values onto five theoretical frameworks, and measure selected-minus-neglected value preferences of LLMs; also tests alignment with OpenAI ModelSpec and Anthropic Constitutional AI principles and steerability via system prompts.

Key results

• LLMs uniformly favor self-expression over survival in World Values Survey and care over loyalty in Moral Foundations Theory.
• Mixtral-8x7B neglects truthfulness by 9.7% while GPT-4-turbo selects it by 9.4%.
• Steering GPT-4-turbo via system prompts is ineffective in general; the authors find end users cannot effectively steer value prioritization.

Measured effects reported in the paper

Surprising findings

• Steering GPT-4-turbo through system prompts that promote specific values fails to shift preferences, with both supporting and opposing prompts producing a shift toward supporting values.
• GPT-4-turbo favors transparency over privacy despite OpenAI's stated privacy principle.

Limitations

• Dataset generated solely by GPT-4, with likely Western cultural bias; validation relies on Reddit r/AITA which is demographically skewed.
• Mixtral-8x7B often refused to choose an action, so its preferences on some values (e.g., purity) are unreliable.
• Greedy decoding and only six models tested; value mapping to five theories is approximate.

Experimental setup

Datasets: Social Chemistry Reddit r/AITA

Benchmarks: DailyDilemmas

Metrics: selected minus neglected value preference (normalized) F1 for dilemma-Reddit similarity Cohen's kappa word-level value overlap with Reddit comments

Baselines: GPT-3.5-turbo Llama-2-70B Claude-3-haiku Mixtral-8x7B

Builds on: Social Chemistry ETHICS Delphi MoralExceptionQA Moral Foundations Theory

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