# DECASTE: Unveiling Caste Stereotypes in Large Language Models through Multi-Dimensional Bias Analysis

arXiv: https://arxiv.org/abs/2505.14971 · HTML: https://mlsanity.com/paper/2505.14971/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [BBQ: A Hand-Built Bias Benchmark for Question Answering](https://mlsanity.com/paper/2110.08193/) (2021)
- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Prometheus: Inducing Fine-grained Evaluation Capability in Language Models](https://mlsanity.com/paper/2310.08491/) (2023)
- [Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models](https://mlsanity.com/paper/2405.01535/) (2024)
- [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://mlsanity.com/paper/1911.03891/) (2019)
- ["Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters](https://mlsanity.com/paper/2310.09219/) (2023)
- [Disclosure and Mitigation of Gender Bias in LLMs](https://mlsanity.com/paper/2402.11190/) (2024)
- [IndiBias: A Benchmark Dataset to Measure Social Biases in Language Models for Indian Context](https://mlsanity.com/paper/2403.20147/) (2024)
- [Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation](https://mlsanity.com/paper/2311.00306/) (2023)
- [Socially Aware Bias Measurements for Hindi Language Representations](https://mlsanity.com/paper/2110.07871/) (2021)
- [Global Voices, Local Biases: Socio-Cultural Prejudices across Languages](https://mlsanity.com/paper/2310.17586/) (2023)
- [Indian-BhED: A Dataset for Measuring India-Centric Biases in Large Language Models](https://mlsanity.com/paper/2309.08573/) (2023)

## Built on by (most cited first, 12 of 12)

- [AMBEDKAR-A Multi-level Bias Elimination through a Decoding Approach with Knowledge Augmentation for Robust Constitutional Alignment of Language Models](https://mlsanity.com/paper/2509.02133/) (2025)
- [MIST: Towards Multi-dimensional Implicit BiaS Evaluation of LLMs for Theory of Mind](https://mlsanity.com/paper/2506.14161/) (2025)
- [Modular Techniques for Synthetic Long-Context Data Generation in Language Model Training and Evaluation](https://mlsanity.com/paper/2509.01185/) (2025)
- [Dual-Metric Evaluation of Social Bias in Large Language Models: Evidence from an Underrepresented Nepali Cultural Context](https://mlsanity.com/paper/2603.07792/) (2026)
- [Redirected, Not Removed: Task-Dependent Stereotyping Reveals the Limits of LLM Alignments](https://mlsanity.com/paper/2604.02669/) (2026)
- [Compounding Disadvantage: Auditing Intersectional Bias in LLM-Generated Explanations Across Indian and American STEM Education](https://mlsanity.com/paper/2601.14506/) (2026)
- [Auditing Disability Representation in Vision-Language Models](https://mlsanity.com/paper/2601.17348/) (2026)
- [SectEval: Evaluating the Latent Sectarian Preferences of Large Language Models](https://mlsanity.com/paper/2603.12768/) (2026)
- [Sima AIunty: Caste Audit in LLM-Driven Matchmaking](https://mlsanity.com/paper/2603.29288/) (2026)
- [ImplicitBBQ: Benchmarking Implicit Bias in Large Language Models through Characteristic Based Cues](https://mlsanity.com/paper/2604.01925/) (2026)
- [Beyond Categories of Caste: Examining Caste Bias and Morality in Text-to-Image AI Models](https://mlsanity.com/paper/2606.00039/) (2026)
- [RupeeBias: Auditing Demographic Bias in Indian Economic Guidance from Large Language Models](https://mlsanity.com/paper/2609.31245/) (2026)

## Abstract (arXiv)

Recent advancements in large language models (LLMs) have revolutionized natural language processing (NLP) and expanded their applications across diverse domains. However, despite their impressive capabilities, LLMs have been shown to reflect and perpetuate harmful societal biases, including those based on ethnicity, gender, and religion. A critical and underexplored issue is the reinforcement of caste-based biases, particularly towards India's marginalized caste groups such as Dalits and Shudras. In this paper, we address this gap by proposing DECASTE, a novel, multi-dimensional framework designed to detect and assess both implicit and explicit caste biases in LLMs. Our approach evaluates caste fairness across four dimensions: socio-cultural, economic, educational, and political, using a range of customized prompting strategies. By benchmarking several state-of-the-art LLMs, we reveal that these models systematically reinforce caste biases, with significant disparities observed in the treatment of oppressed versus dominant caste groups. For example, bias scores are notably elevated when comparing Dalits and Shudras with dominant caste groups, reflecting societal prejudices that persist in model outputs. These results expose the subtle yet pervasive caste biases in LLMs and emphasize the need for more comprehensive and inclusive bias evaluation methodologies that assess the potential risks of deploying such models in real-world contexts.