# Can Large Language Models Make Everyone Happy?

arXiv: https://arxiv.org/abs/2602.11091 · HTML: https://mlsanity.com/paper/2602.11091/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 17 of 17)

- [BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset](https://mlsanity.com/paper/2307.04657/) (2023)
- [Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences](https://mlsanity.com/paper/2404.12272/) (2024)
- [Fine-Tuned 'Small' LLMs (Still) Significantly Outperform Zero-Shot Generative AI Models in Text Classification](https://mlsanity.com/paper/2406.08660/) (2024)
- [MIB: A Mechanistic Interpretability Benchmark](https://mlsanity.com/paper/2504.13151/) (2025)
- [Mind the Value-Action Gap: Do LLMs Act in Alignment with Their Values?](https://mlsanity.com/paper/2501.15463/) (2025)
- [Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability](https://mlsanity.com/paper/2501.01346/) (2025)
- [Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations](https://mlsanity.com/paper/2308.08162/) (2023)
- [ValueCompass: A Framework for Measuring Contextual Value Alignment Between Human and LLMs](https://mlsanity.com/paper/2409.09586/) (2024)
- [H3Fusion: Helpful, Harmless, Honest Fusion of Aligned LLMs](https://mlsanity.com/paper/2411.17792/) (2024)
- [From Neurons to Semantics: Evaluating Cross-Linguistic Alignment Capabilities of Large Language Models via Neurons Alignment](https://mlsanity.com/paper/2507.14900/) (2025)
- [Too Helpful, Too Harmless, Too Honest or Just Right?](https://mlsanity.com/paper/2509.08486/) (2025)
- [An Investigation into Value Misalignment in LLM-Generated Texts for Cultural Heritage](https://mlsanity.com/paper/2501.02039/) (2025)
- [Mechanistic Interpretability for Large Language Model Alignment: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2602.11180/) (2026)
- [Benchmarking and Improving LLM Robustness for Personalized Generation](https://mlsanity.com/paper/2509.19358/) (2025)
- [Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions](https://mlsanity.com/paper/2502.08657/) (2025)
- [SafeTuneBed: A Toolkit for Benchmarking LLM Safety Alignment in Fine-Tuning](https://mlsanity.com/paper/2506.00676/) (2025)
- [Evaluating LLMs for Demographic-Targeted Social Bias Detection: A Comprehensive Benchmark Study](https://mlsanity.com/paper/2510.04641/) (2025)

## Abstract (arXiv)

Misalignment in Large Language Models (LLMs) refers to the failure to simultaneously satisfy safety, value, and cultural dimensions, leading to behaviors that diverge from human expectations in real-world settings where these dimensions must co-occur. Existing benchmarks, such as SAFETUNEBED (safety-centric), VALUEBENCH (value-centric), and WORLDVIEW-BENCH (culture-centric), primarily evaluate these dimensions in isolation and therefore provide limited insight into their interactions and trade-offs. More recent efforts, including MIB and INTERPRETABILITY BENCHMARK-based on mechanistic interpretability, offer valuable perspectives on model failures; however, they remain insufficient for systematically characterizing cross-dimensional trade-offs. To address these gaps, we introduce MisAlign-Profile, a unified benchmark for measuring misalignment trade-offs inspired by mechanistic profiling. First, we construct MISALIGNTRADE, an English misaligned-aligned dataset across 112 normative domains taxonomies, including 14 safety, 56 value, and 42 cultural domains. In addition to domain labels, each prompt is classified with one of three orthogonal semantic types-object, attribute, or relations misalignment-using Gemma-2-9B-it and expanded via Qwen3-30B-A3B-Instruct-2507 with SimHash-based fingerprinting to avoid deduplication. Each prompt is paired with misaligned and aligned responses through two-stage rejection sampling to ensure quality. Second, we benchmark general-purpose, fine-tuned, and open-weight LLMs on MISALIGNTRADE-revealing 12%-34% misalignment trade-offs across dimensions.