# Tears or Cheers? Benchmarking LLMs via Culturally Elicited Distinct Affective Responses

arXiv: https://arxiv.org/abs/2601.13024 · HTML: https://mlsanity.com/paper/2601.13024/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 68 references that are in the map.
- First posted on arXiv 19 Jan 2026.

## Builds on (most cited first, 30 of 41)

- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024)
- [HuatuoGPT, towards Taming Language Model to Be a Doctor](https://mlsanity.com/paper/2305.15075/) (2023)
- [Having Beer after Prayer? Measuring Cultural Bias in Large Language Models](https://mlsanity.com/paper/2305.14456/) (2023)
- [CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark](https://mlsanity.com/paper/2406.05967/) (2024)
- [Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration](https://mlsanity.com/paper/2406.15951/) (2024)
- [Benchmarking Vision Language Models for Cultural Understanding](https://mlsanity.com/paper/2407.10920/) (2024)
- [CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologies](https://mlsanity.com/paper/2404.15238/) (2024)
- [When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Tasks](https://mlsanity.com/paper/2305.06626/) (2023)
- [Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking](https://mlsanity.com/paper/2402.09369/) (2024)
- [CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting](https://mlsanity.com/paper/2404.10199/) (2024)
- [Knowledge of cultural moral norms in large language models](https://mlsanity.com/paper/2306.01857/) (2023)
- [Evaluating Cultural and Social Awareness of LLM Web Agents](https://mlsanity.com/paper/2410.23252/) (2024)
- [Aya Vision: Advancing the Frontier of Multilingual Multimodality](https://mlsanity.com/paper/2505.08751/) (2025)
- [PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation](https://mlsanity.com/paper/2407.05721/) (2024)
- [Omni-Emotion: Extending Video MLLM with Detailed Face and Audio Modeling for Multimodal Emotion Analysis](https://mlsanity.com/paper/2501.09502/) (2025)
- [Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs](https://mlsanity.com/paper/2502.08045/) (2025)
- [FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture](https://mlsanity.com/paper/2406.11030/) (2024)
- [Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning](https://mlsanity.com/paper/2508.10848/) (2025)
- [JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation](https://mlsanity.com/paper/2410.17250/) (2024)
- [No Culture Left Behind: ArtELingo-28, a Benchmark of WikiArt with Captions in 28 Languages](https://mlsanity.com/paper/2411.03769/) (2024)
- [Commonsense Reasoning in Arab Culture](https://mlsanity.com/paper/2502.12788/) (2025)
- [GIMMICK -- Globally Inclusive Multimodal Multitask Cultural Knowledge Benchmarking](https://mlsanity.com/paper/2502.13766/) (2025)
- [CARE: Multilingual Human Preference Learning for Cultural Awareness](https://mlsanity.com/paper/2504.05154/) (2025)
- [CulturalFrames: Assessing Cultural Expectation Alignment in Text-to-Image Models and Evaluation Metrics](https://mlsanity.com/paper/2506.08835/) (2025)
- [CULEMO: Cultural Lenses on Emotion -- Benchmarking LLMs for Cross-Cultural Emotion Understanding](https://mlsanity.com/paper/2503.10688/) (2025)
- [NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities](https://mlsanity.com/paper/2505.18383/) (2025)
- [Beyond Emotion Recognition: A Multi-Turn Multimodal Emotion Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2508.16859/) (2025)

## Built on by (most cited first, 6 of 6)

- [Expressing Social Emotions: Misalignment Between LLMs and Human Cultural Emotion Norms](https://mlsanity.com/paper/2604.16757/) (2026)
- [MMHBench: A Multi-Perspective Benchmark for Mental Health Understanding in Long-Form Videos](https://mlsanity.com/paper/2607.27895/) (2026)
- [Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse](https://mlsanity.com/paper/2608.10810/) (2026)
- [CogEvolution: A Human-like Generative Educational Agent to Simulate Student's Cognitive Evolution](https://mlsanity.com/paper/2604.14786/) (2026)
- [ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction](https://mlsanity.com/paper/2604.23813/) (2026)
- [ArtECulture: Benchmarking Culture-Conditioned Visual Emotion Understanding in Multimodal Large Language Models](https://mlsanity.com/paper/2608.03358/) (2026)

## Abstract (arXiv)

Culture serves as a fundamental determinant of human affective processing and profoundly shapes how individuals perceive and interpret emotional stimuli. Despite this intrinsic link extant evaluations regarding cultural alignment within Large Language Models primarily prioritize declarative knowledge such as geographical facts or established societal customs. These benchmarks remain insufficient to capture the subjective interpretative variance inherent to diverse sociocultural lenses. To address this limitation, we introduce CEDAR, a multimodal benchmark constructed entirely from scenarios capturing Culturally underlinetextscElicited underlinetextscDistinct underlinetextscAffective underlinetextscResponses. To construct CEDAR, we implement a novel pipeline that leverages LLM-generated provisional labels to isolate instances yielding cross-cultural emotional distinctions, and subsequently derives reliable ground-truth annotations through rigorous human evaluation. The resulting benchmark comprises 10,962 instances across seven languages and 14 fine-grained emotion categories, with each language including 400 multimodal and 1,166 text-only samples. Comprehensive evaluations of 17 representative multilingual models reveal a dissociation between language consistency and cultural alignment, demonstrating that culturally grounded affective understanding remains a significant challenge for current models.