Can Large Language Models Make Everyone Happy?

• URL: http://arxiv.org/abs/2602.11091v1
• Date: Wed, 11 Feb 2026 17:57:23 GMT
• Title: Can Large Language Models Make Everyone Happy?
• Authors: Usman Naseem, Gautam Siddharth Kashyap, Ebad Shabbir, Sushant Kumar Ray, Abdullah Mohammad, Rafiq Ali,
• Abstract summary: Misalignment in Large Language Models (LLMs) refers to the failure to simultaneously satisfy safety, value, and cultural dimensions.<n>We introduce MisAlign-Profile, a unified benchmark for measuring misalignment trade-offs inspired by mechanistic profiling.
• Score: 12.59854280011403
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Manifold of Failure: Behavioral Attraction Basins in Language Models [0.49388902330345724]
  This paper introduces a framework for systematically mapping the Manifold of Failure in Large Language Models (LLMs)<n>We reframe the search for vulnerabilities as a quality diversity problem, using MAP-Elites to illuminate the continuous topology of these failure regions.<n>Across three LLMs, we show that MAP-Elites achieves up to 63% behavioral coverage, discovers up to 370 distinct vulnerability niches, and reveals dramatically different model-specific topological signatures.
  arXiv  Detail & Related papers  (2026-02-25T15:08:20Z)
• Position: General Alignment Has Hit a Ceiling; Edge Alignment Must Be Taken Seriously [51.03213216886717]
  We take the position that the dominant paradigm of General Alignment reaches a structural ceiling in settings with conflicting values.<n>We introduce Edge Alignment as a distinct approach in which systems preserve multi dimensional value structure.
  arXiv  Detail & Related papers  (2026-02-23T16:51:43Z)
• Narrow fine-tuning erodes safety alignment in vision-language agents [0.12441041004077093]
  Lifelong multimodal agents must continuously adapt to new tasks through post-training.<n>We demonstrate that fine-tuning aligned vision-language models on narrow-domain harmful datasets induces severe emergent misalignment.
  arXiv  Detail & Related papers  (2026-02-18T22:47:28Z)
• Are Aligned Large Language Models Still Misaligned? [13.062124372682106]
  Mis-Align Bench is a unified benchmark for analyzing misalignment across safety, value, and cultural dimensions.<n> SAVACU is an English-aligned dataset of 382,424 misaligned samples spanning 112 domains (or labels)
  arXiv  Detail & Related papers  (2026-02-11T19:30:43Z)
• Do Large Language Models Reflect Demographic Pluralism in Safety? [12.59854280011403]
  Large Language Model (LLM) safety is inherently pluralistic, reflecting variations in moral norms, cultural expectations, and demographic contexts.<n>Demo-SafetyBench addresses this gap by modeling demographic pluralism directly at the prompt level, decoupling value framing from responses.<n>Stage I, prompts from DICES are reclassified into 14 safety domains using Mistral 7B-Instruct-v0.3, retaining demographic metadata and expanding low-resource domains.<n>Stage II, pluralistic sensitivity is evaluated using LLMs-as-Raters-Gemma-7B, GPT-4o, and LLaMA-2-7B-under zero-shot inference
  arXiv  Detail & Related papers  (2026-02-07T05:40:10Z)
• Health-ORSC-Bench: A Benchmark for Measuring Over-Refusal and Safety Completion in Health Context [82.32380418146656]
  Health-ORSC-Bench is the first large-scale benchmark designed to measure textbfOver-Refusal and textbfSafe Completion quality in healthcare.<n>Our framework uses an automated pipeline with human validation to test models at varying levels of intent ambiguity.<n>Health-ORSC-Bench provides a rigorous standard for calibrating the next generation of medical AI assistants.
  arXiv  Detail & Related papers  (2026-01-25T01:28:52Z)
• When in Doubt, Deliberate: Confidence-Based Routing to Expert Debate for Sexism Detection [7.299050989302629]
  We propose a framework to address the combined effects of (i) underrepresentation, (ii) noise, and (iii) conceptual ambiguity in both data and model predictions.<n>Our approach achieves state-of-the-art results across several benchmarks, with a +2.72% improvement in F1 on Tasks EXIST 2025 Task 1.1, and a gains of +4.48% and +1.30% on EDOS A and B, respectively.
  arXiv  Detail & Related papers  (2025-12-21T05:48:57Z)
• CIFE: Code Instruction-Following Evaluation [3.941243815951084]
  We introduce a benchmark of 1,000 Python tasks, each paired with an average of 7 developer-specified constraints spanning 13 categories.<n>We evaluate 14 open- and closed-source models using complementary adherence metrics and propose the C2A Score, a composite measure that jointly captures correctness and constraint compliance.<n>Results reveal a substantial gap between partial and strict satisfaction, while strong models achieve over 90% partial adherence, strict adherence remains between 39-66%.
  arXiv  Detail & Related papers  (2025-12-19T09:43:20Z)
• DUAL-Bench: Measuring Over-Refusal and Robustness in Vision-Language Models [59.45605332033458]
  Safety mechanisms can backfire, causing over-refusal, where models decline benign requests out of excessive caution.<n>No existing benchmark has systematically addressed over-refusal in the visual modality.<n>This setting introduces unique challenges, such as dual-use cases where an instruction is harmless, but the accompanying image contains harmful content.
  arXiv  Detail & Related papers  (2025-10-12T23:21:34Z)
• mFARM: Towards Multi-Faceted Fairness Assessment based on HARMs in Clinical Decision Support [10.90604216960609]
  The deployment of Large Language Models (LLMs) in high-stakes medical settings poses a critical AI alignment challenge.<n>Existing fairness evaluation methods fall short in these contexts as they typically use simplistic metrics that overlook the multi-dimensional nature of medical harms.<n>We propose a multi-metric framework - Multi-faceted Fairness Assessment based on hARMs ($mFARM$) to audit fairness for three distinct dimensions of disparity.<n>Our findings showcase that the proposed $mFARM$ metrics capture subtle biases more effectively under various settings.
  arXiv  Detail & Related papers  (2025-09-02T06:47:57Z)
• TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data Synthesis [35.2545408706656]
  Large Language Models (LLMs) excel in various natural language processing tasks but remain vulnerable to generating harmful content or being exploited for malicious purposes.<n>We propose a novel analysis framework to measure the risk coverage of alignment datasets across three essential dimensions: Lexical Diversity, Malicious Intent, and Jailbreak Tactics.
  arXiv  Detail & Related papers  (2025-05-30T15:02:21Z)
• Multimodal Cultural Safety: Evaluation Frameworks and Alignment Strategies [58.88053690412802]
  Large vision-language models (LVLMs) are increasingly deployed in globally distributed applications, such as tourism assistants.<n> CROSS is a benchmark designed to assess the cultural safety reasoning capabilities of LVLMs.<n>We evaluate 21 leading LVLMs, including mixture-of-experts models and reasoning models.
  arXiv  Detail & Related papers  (2025-05-20T23:20:38Z)
• Do We Really Need Curated Malicious Data for Safety Alignment in Multi-modal Large Language Models? [83.53005932513155]
  Multi-modal large language models (MLLMs) have made significant progress, yet their safety alignment remains limited.<n>We propose finetuning MLLMs on a small set of benign instruct-following data with responses replaced by simple, clear rejection sentences.
  arXiv  Detail & Related papers  (2025-04-14T09:03:51Z)
• Emulated Disalignment: Safety Alignment for Large Language Models May Backfire! [65.06450319194454]
  Large language models (LLMs) undergo safety alignment to ensure safe conversations with humans. This paper introduces a training-free attack method capable of reversing safety alignment. We name this method emulated disalignment (ED) because sampling from this contrastive distribution provably emulates the result of fine-tuning to minimize a safety reward.
  arXiv  Detail & Related papers  (2024-02-19T18:16:51Z)
• Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets [92.38654521870444]
  We introduce ACES, a contrastive challenge set spanning 146 language pairs. This dataset aims to discover whether metrics can identify 68 translation accuracy errors. We conduct a large-scale study by benchmarking ACES on 50 metrics submitted to the WMT 2022 and 2023 metrics shared tasks.
  arXiv  Detail & Related papers  (2024-01-29T17:17:42Z)
• Flames: Benchmarking Value Alignment of LLMs in Chinese [86.73527292670308]
  This paper proposes a value alignment benchmark named Flames. It encompasses both common harmlessness principles and a unique morality dimension that integrates specific Chinese values. Our findings indicate that all the evaluated LLMs demonstrate relatively poor performance on Flames.
  arXiv  Detail & Related papers  (2023-11-12T17:18:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.