# BengaliMoralBench: A Benchmark for Auditing Moral Reasoning in Large Language Models within Bengali Language and Culture

arXiv: https://arxiv.org/abs/2511.03180 · HTML: https://mlsanity.com/paper/2511.03180/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 33 of its 65 references that are in the map.
- First posted on arXiv 5 Nov 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 30 of 33)

- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Aligning AI With Shared Human Values](https://mlsanity.com/paper/2008.02275/) (2020)
- [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://mlsanity.com/paper/2203.09509/) (2022)
- [Cultural Bias and Cultural Alignment of Large Language Models](https://mlsanity.com/paper/2311.14096/) (2023)
- [Continual Learning for Large Language Models: A Survey](https://mlsanity.com/paper/2402.01364/) (2024)
- [Re-imagining Algorithmic Fairness in India and Beyond](https://mlsanity.com/paper/2101.09995/) (2021)
- [Survey of Cultural Awareness in Language Models: Text and Beyond](https://mlsanity.com/paper/2411.00860/) (2024)
- [INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge](https://mlsanity.com/paper/2411.19799/) (2024)
- [AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances](https://mlsanity.com/paper/2409.11360/) (2024)
- [Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs](https://mlsanity.com/paper/2503.08688/) (2025)
- [MoralBench: Moral Evaluation of LLMs](https://mlsanity.com/paper/2406.04428/) (2024)
- [NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models](https://mlsanity.com/paper/2404.12464/) (2024)
- [The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating It](https://mlsanity.com/paper/2505.24119/) (2025)
- [Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://mlsanity.com/paper/2406.03930/) (2024)
- [LLM Ethics Benchmark: A Three-Dimensional Assessment System for Evaluating Moral Reasoning in Large Language Models](https://mlsanity.com/paper/2505.00853/) (2025)
- [The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biases](https://mlsanity.com/paper/2401.10535/) (2024)
- [TigerLLM - A Family of Bangla Large Language Models](https://mlsanity.com/paper/2503.10995/) (2025)
- [SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use](https://mlsanity.com/paper/2505.17332/) (2025)
- [How Deep Is Representational Bias in LLMs? The Cases of Caste and Religion](https://mlsanity.com/paper/2508.03712/) (2025)
- [Decolonial AI Alignment: Openness, Vi'sedsa-Dharma, and Including Excluded Knowledges](https://mlsanity.com/paper/2309.05030/) (2023)
- [The Multilingual Divide and Its Impact on Global AI Safety](https://mlsanity.com/paper/2505.21344/) (2025)
- [Do You Know About My Nation? Investigating Multilingual Language Models' Cultural Literacy Through Factual Knowledge](https://mlsanity.com/paper/2511.00657/) (2025)
- [BnMMLU: Measuring Massive Multitask Language Understanding in Bengali](https://mlsanity.com/paper/2505.18951/) (2025)
- [Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation](https://mlsanity.com/paper/2504.07072/) (2025)
- [Nunchi-Bench: Benchmarking Language Models on Cultural Reasoning with a Focus on Korean Superstition](https://mlsanity.com/paper/2507.04014/) (2025)
- [We Politely Insist: Your LLM Must Learn the Persian Art of Taarof](https://mlsanity.com/paper/2509.01035/) (2025)
- [WorldView-Bench: A Benchmark for Evaluating Global Cultural Perspectives in Large Language Models](https://mlsanity.com/paper/2505.09595/) (2025)
- [Exploring Bengali Religious Dialect Biases in Large Language Models with Evaluation Perspectives](https://mlsanity.com/paper/2407.18376/) (2024)

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

- [Register Shifts Break LLM Safety: A Bengali Benchmark with Culturally Grounded Harms](https://mlsanity.com/paper/2608.22335/) (2026)

## Abstract (arXiv)

As multilingual Large Language Models (LLMs) gain traction across South Asia, their alignment with local ethical norms, particularly for Bengali, spoken by over 285 million people worldwide and among the most widely spoken languages globally, remains underexplored. Existing ethics benchmarks are predominantly English-centric and shaped by Western moral frameworks, overlooking cultural nuances vital for real-world deployment. To address this gap, we introduce BengaliMoralBench, a large-scale ethics benchmark designed for Bengali language and sociocultural contexts. Our benchmark spans five moral domains: (1) Daily Activities, (2) Habits, (3) Parenting, (4) Family Relationships, and (5) Religious Activities, each subdivided into ten culturally grounded categories, totaling 50 subtopics. Each scenario is annotated through native-speaker consensus under three ethical lenses: virtue ethics, commonsense ethics, and justice ethics. We conduct a systematic zero-shot evaluation under a unified prompting protocol across both open-weight and closed-source models, including recent Llama and Gemma variants, Qwen and DeepSeek models, frontier models (GPT-4o-mini and Gemini 1.5 Pro), and a large multilingual baseline (Qwen3-Next-80B). Results show substantial variation in performance across lenses and domains, and our qualitative analysis reveals persistent weaknesses in cultural grounding, commonsense reasoning, and moral fairness. These findings expose critical limitations of current LLMs in non-Western settings and underscore the need for culturally grounded evaluation. BengaliMoralBench provides a foundation for responsible localization and benchmarking to support the deployment of language technologies in culturally diverse, low-resource markets such as Bangladesh.