# D2VBench: Benchmarking Large Language Models with Value Dilemmas in Daily Scenarios

arXiv: https://arxiv.org/abs/2607.19834 · HTML: https://mlsanity.com/paper/2607.19834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 37 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 23 of 23)

- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://mlsanity.com/paper/2009.11462/) (2020)
- [GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models](https://mlsanity.com/paper/2508.06471/) (2025)
- [BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation](https://mlsanity.com/paper/2101.11718/) (2021)
- [Large Language Model Alignment: A Survey](https://mlsanity.com/paper/2309.15025/) (2023)
- [Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets](https://mlsanity.com/paper/2106.10328/) (2021)
- [ProsocialDialog: A Prosocial Backbone for Conversational Agents](https://mlsanity.com/paper/2205.12688/) (2022)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility](https://mlsanity.com/paper/2307.09705/) (2023)
- [Moral Foundations of Large Language Models](https://mlsanity.com/paper/2310.15337/) (2023)
- [STAIR: Improving Safety Alignment with Introspective Reasoning](https://mlsanity.com/paper/2502.02384/) (2025)
- [DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of Daily Life](https://mlsanity.com/paper/2410.02683/) (2024)
- [Language Model Alignment in Multilingual Trolley Problems](https://mlsanity.com/paper/2407.02273/) (2024)
- [KorNAT: LLM Alignment Benchmark for Korean Social Values and Common Knowledge](https://mlsanity.com/paper/2402.13605/) (2024)
- [Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits](https://mlsanity.com/paper/2301.00355/) (2023)
- [Denevil: Towards Deciphering and Navigating the Ethical Values of Large Language Models via Instruction Learning](https://mlsanity.com/paper/2310.11053/) (2023)
- [Normative Evaluation of Large Language Models with Everyday Moral Dilemmas](https://mlsanity.com/paper/2501.18081/) (2025)
- [Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework](https://mlsanity.com/paper/2506.14948/) (2025)
- [Mapping Topics in 100,000 Real-life Moral Dilemmas](https://mlsanity.com/paper/2203.16762/) (2022)
- [CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language Models](https://mlsanity.com/paper/2408.09819/) (2024)
- [MORABLES: A Benchmark for Assessing Abstract Moral Reasoning in LLMs with Fables](https://mlsanity.com/paper/2509.12371/) (2025)
- [MedEthicEval: Evaluating Large Language Models Based on Chinese Medical Ethics](https://mlsanity.com/paper/2503.02374/) (2025)
- [Benchmarking Multi-National Value Alignment for Large Language Models](https://mlsanity.com/paper/2504.12911/) (2025)
- [Decoding the Mind of Large Language Models: A Quantitative Evaluation of Ideology and Biases](https://mlsanity.com/paper/2505.12183/) (2025)

## Built on by (most cited first, 1 of 1)

- [PlurVA-LLM-2026 Shared Task Track-1: Pluralistic Value Alignment in LLMs via Multilingual Fine-Tuning and Threshold Calibration](https://mlsanity.com/paper/2609.32382/) (2026)

## Abstract (arXiv)

With the wide application of large language models (LLMs) in real-world scenarios, the value implication of their outputs is crucial. However, existing evaluation benchmarks suffer from insufficient coverage of value dilemmas in daily scenarios involving multiple value conflicts and simplistic evaluation formalisms that fail to assess LLMs' value alignment. To address these issues, we propose D2VBench, a value alignment benchmark comprising 10,000 instances of real daily dilemma scenarios constructed through a multi-stage collaboration between LLMs and humans, grounded in 158 manually annotated fine-grained value concepts. For evaluation on the benchmark, we present a hybrid evaluation paradigm that integrates multiple-choice questions with open-ended questions. We conduct comprehensive evaluations on eight mainstream LLMs. Experimental results demonstrate that D2VBench exhibits high reliability and robustness, effectively reflecting the LLMs' alignment across different value categories and dimensions, and providing a more realistic and fine-grained tool for research on value alignment. The dataset is available at https://github.com/tjunlp-lab/D2VBench.