[Paper] Can Large Language Models Make Everyone Happy?

Source: arXiv - 2602.11091v1

Overview

The paper “Can Large Language Models Make Everyone Happy?” tackles a growing concern in the AI community: misalignment—the inability of large language models (LLMs) to simultaneously satisfy safety, value, and cultural expectations. By introducing a unified benchmark called MisAlign‑Profile, the authors reveal how current models trade off between these dimensions, exposing systematic gaps that existing single‑focus tests miss.

Key Contributions

• MisAlign‑Profile benchmark: A first‑of‑its‑kind dataset (MISALIGNTRADE) covering 112 normative domains (14 safety, 56 value, 42 cultural) with richly annotated prompts.
• Semantic misalignment typing: Each prompt is labeled as an object, attribute, or relation misalignment, enabling fine‑grained analysis of failure modes.
• High‑quality aligned vs. misaligned response pairs: Generated via a two‑stage rejection‑sampling pipeline that guarantees comparable fluency while differing in alignment.
• Comprehensive evaluation: Benchmarks a spectrum of LLMs—from open‑weight models (e.g., Gemma‑2‑9B‑IT, Qwen3‑30B‑A3B‑Instruct) to fine‑tuned commercial systems—showing 12‑34 % trade‑off rates across safety, value, and cultural dimensions.
• Mechanistic profiling inspiration: Leverages fingerprinting (SimHash) and model‑driven expansion to ensure diversity and avoid duplicate prompts, mirroring techniques from interpretability research.

Methodology

1. Domain Taxonomy Construction – The authors curated 112 normative domains by merging existing safety, value, and cultural taxonomies.
2. Prompt Generation – Starting with a seed set, they used Gemma‑2‑9B‑IT to generate initial prompts, then expanded them with Qwen3‑30B‑A3B‑Instruct‑2507. SimHash fingerprinting filtered out near‑duplicates, preserving semantic variety.
3. Semantic Typing – Each prompt received one of three orthogonal tags:
   • Object misalignment (e.g., “Should the model recommend a harmful product?”)
   • Attribute misalignment (e.g., “Is it okay to lie about a user’s age?”)
   • Relation misalignment (e.g., “Should the model side‑track a conversation to avoid a taboo topic?”)
4. Response Pair Creation – For every prompt, the pipeline generated aligned and misaligned completions. A two‑stage rejection sampling loop kept only pairs that passed fluency checks but diverged on the targeted alignment dimension.
5. Benchmarking – The final dataset (MISALIGNTRADE) was used to evaluate a suite of LLMs. Performance was measured as the proportion of cases where a model’s output favored one dimension at the expense of another (e.g., safe but culturally insensitive).

Results & Findings

• Trade‑off prevalence: Across all tested models, 12 %–34 % of prompts exhibited a clear misalignment trade‑off, confirming that current LLMs rarely satisfy safety, value, and cultural constraints simultaneously.
• Model size vs. alignment: Larger open‑weight models (e.g., Qwen3‑30B) showed modest improvements over smaller ones but still suffered notable trade‑offs, suggesting that scaling alone does not solve the problem.
• Fine‑tuning impact: Models fine‑tuned on safety‑centric data reduced safety violations but often introduced cultural or value misalignments, highlighting the “zero‑sum” nature of current alignment techniques.
• Semantic type patterns: Relation misalignments were the hardest to resolve (highest trade‑off rates), while object misalignments were comparatively easier for models to handle.

Practical Implications

• Product teams should treat alignment as a multi‑objective optimization problem rather than a single safety checklist. The MisAlign‑Profile benchmark can serve as a diagnostic tool to surface hidden trade‑offs before deployment.
• Prompt engineers can use the semantic misalignment tags to craft more robust prompts that explicitly steer models away from high‑risk relation‑type failures.
• Fine‑tuning pipelines may need to incorporate multi‑dimensional reward modeling (e.g., reinforcement learning with safety, value, and cultural reward components) to balance competing norms.
• Regulatory compliance: The benchmark’s coverage of cultural domains aligns with emerging global AI governance frameworks that require respect for local norms, making it valuable for audit trails.
• Open‑source community: By releasing the dataset and evaluation scripts, the authors enable developers to benchmark new architectures (e.g., retrieval‑augmented LLMs) for alignment trade‑offs out‑of‑the‑box.

Limitations & Future Work

• English‑only scope: MISALIGNTRADE currently targets English prompts, limiting insights into multilingual or low‑resource cultural contexts.
• Static taxonomy: The 112 domains are fixed; real‑world norms evolve, so periodic updates will be needed to keep the benchmark relevant.
• Human evaluation depth: While the two‑stage rejection sampling ensures quality, deeper human judgments (e.g., cross‑cultural panels) could better validate the nuanced trade‑offs.
• Mechanistic explanations: The paper surfaces trade‑offs but does not fully explain why models favor one dimension over another; future work could integrate interpretability tools to trace internal decision pathways.

Bottom line: The MisAlign‑Profile benchmark shines a light on the hidden tug‑of‑war between safety, values, and culture in today’s LLMs, offering developers a practical yardstick to measure and improve multi‑dimensional alignment before their models go live.

Authors

• Usman Naseem
• Gautam Siddharth Kashyap
• Ebad Shabbir
• Sushant Kumar Ray
• Abdullah Mohammad
• Rafiq Ali

Paper Information

• arXiv ID: 2602.11091v1
• Categories: cs.CL
• Published: February 11, 2026
• PDF: Download PDF