# A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions

arXiv: https://arxiv.org/abs/2409.16430 · HTML: https://mlsanity.com/paper/2409.16430/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 35 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [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)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://mlsanity.com/paper/1707.09457/) (2017)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies](https://mlsanity.com/paper/2304.07683/) (2023)
- [Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception](https://mlsanity.com/paper/2403.14896/) (2024)
- [Bias Testing and Mitigation in LLM-based Code Generation](https://mlsanity.com/paper/2309.14345/) (2023)
- [Measuring Implicit Bias in Explicitly Unbiased Large Language Models](https://mlsanity.com/paper/2402.04105/) (2024)
- [Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting](https://mlsanity.com/paper/2401.15585/) (2024)
- [Bias patterns in the application of LLMs for clinical decision support: A comprehensive study](https://mlsanity.com/paper/2404.15149/) (2024)
- [(Ir)rationality and Cognitive Biases in Large Language Models](https://mlsanity.com/paper/2402.09193/) (2024)
- [BiasAlert: A Plug-and-play Tool for Social Bias Detection in LLMs](https://mlsanity.com/paper/2407.10241/) (2024)
- [Breaking Bias, Building Bridges: Evaluation and Mitigation of Social Biases in LLMs via Contact Hypothesis](https://mlsanity.com/paper/2407.02030/) (2024)
- [Large Language Model (LLM) Bias Index -- LLMBI](https://mlsanity.com/paper/2312.14769/) (2023)
- [Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction](https://mlsanity.com/paper/2403.09963/) (2024)
- [PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language Models](https://mlsanity.com/paper/2404.08699/) (2024)
- [Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLP](https://mlsanity.com/paper/2404.19071/) (2024)
- [Comprehensive Study on Sentiment Analysis: From Rule-based to modern LLM based system](https://mlsanity.com/paper/2409.09989/) (2024)
- [Are LLMs Rational Investors? A Study on Detecting and Reducing the Financial Bias in LLMs](https://mlsanity.com/paper/2402.12713/) (2024)
- [Evaluation of LLMs Biases Towards Elite Universities: A Persona-Based Exploration](https://mlsanity.com/paper/2407.12801/) (2024)
- [The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models](https://mlsanity.com/paper/2404.08760/) (2024)
- [Born With a Silver Spoon? Investigating Socioeconomic Bias in Large Language Models](https://mlsanity.com/paper/2403.14633/) (2024)
- [LangBiTe: A Platform for Testing Bias in Large Language Models](https://mlsanity.com/paper/2404.18558/) (2024)

## Built on by (most cited first, 27 of 27)

- [Alignment Revisited: Are Large Language Models Consistent in Stated and Revealed Preferences?](https://mlsanity.com/paper/2506.00751/) (2025)
- [Colombian Waitresses y Jueces canadienses: Gender and Country Biases in Occupation Recommendations from LLMs](https://mlsanity.com/paper/2505.02456/) (2025)
- [Emergently Misaligned Language Models Show Behavioral Self-Awareness That Shifts With Subsequent Realignment](https://mlsanity.com/paper/2602.14777/) (2026)
- [Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System](https://mlsanity.com/paper/2502.07254/) (2025)
- [Mind the Language Gap: Automated and Augmented Evaluation of Bias in LLMs for High- and Low-Resource Languages](https://mlsanity.com/paper/2504.18560/) (2025)
- [Bias after Prompting: Persistent Discrimination in Large Language Models](https://mlsanity.com/paper/2509.08146/) (2025)
- [Building Trust in Clinical LLMs: Bias Analysis and Dataset Transparency](https://mlsanity.com/paper/2510.18556/) (2025)
- [Comprehensive Framework for Evaluating Conversational AI Chatbots](https://mlsanity.com/paper/2502.06105/) (2025)
- [CALM: Culturally Self-Aware Language Models](https://mlsanity.com/paper/2601.03483/) (2026)
- [Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection](https://mlsanity.com/paper/2601.05403/) (2026)
- [Agent-Enhanced Large Language Models for Researching Political Institutions](https://mlsanity.com/paper/2503.13524/) (2025)
- ['Rich Dad, Poor Lad': How do Large Language Models Contextualize Socioeconomic Factors in College Admission ?](https://mlsanity.com/paper/2509.16400/) (2025)
- [MLLM-as-a-Judge Exhibits Model Preference Bias](https://mlsanity.com/paper/2604.11589/) (2026)
- [Towards Efficient and Effective Alignment of Large Language Models](https://mlsanity.com/paper/2506.09329/) (2025)
- [A Multi-Agent Probabilistic Inference Framework Inspired by Kairanban-Style CoT System with IdoBata Conversation for Debiasing](https://mlsanity.com/paper/2506.21565/) (2025)
- [Audit, Alignment, and Optimization of LM-Powered Subroutines with Application to Public Comment Processing](https://mlsanity.com/paper/2507.08109/) (2025)
- [Do They Understand Them? An Updated Evaluation on Nonbinary Pronoun Handling in Large Language Models](https://mlsanity.com/paper/2508.00788/) (2025)
- [A Close Reading Approach to Gender Narrative Biases in AI-Generated Stories](https://mlsanity.com/paper/2508.09651/) (2025)
- [The GDN-CC Dataset: Automatic Corpus Clarification for AI-enhanced Democratic Citizen Consultations](https://mlsanity.com/paper/2601.14944/) (2026)
- [Measuring Political Stance and Consistency in Large Language Models](https://mlsanity.com/paper/2601.17016/) (2026)
- [Why Do Large Language Models Generate Harmful Content?](https://mlsanity.com/paper/2604.11663/) (2026)
- [First, Do No Harm (With LLMs): Mitigating Racial Bias via Agentic Workflows](https://mlsanity.com/paper/2604.18038/) (2026)
- [Saying More Than They Know: A Framework for Quantifying Epistemic-Rhetorical Miscalibration in Large Language Models](https://mlsanity.com/paper/2604.19768/) (2026)
- [Fairness-Aware Retrieval Optimization for Retrieval-Augmented Generation](https://mlsanity.com/paper/2605.15790/) (2026)
- [Curation of a Cardiology Interface Terminology for Highlighting Electronic Health Records using Machine Learning](https://mlsanity.com/paper/2606.08311/) (2026)
- [GAS-Leak-LLM: Genetic Algorithm-Based Suffix Optimization for Black-Box LLM Jailbreaking](https://mlsanity.com/paper/2606.15788/) (2026)
- [Wasted large language models: A life cycle thinking approach](https://mlsanity.com/paper/2608.17055/) (2026)

## Abstract (arXiv)

Large Language Models(LLMs) have revolutionized various applications in natural language processing (NLP) by providing unprecedented text generation, translation, and comprehension capabilities. However, their widespread deployment has brought to light significant concerns regarding biases embedded within these models. This paper presents a comprehensive survey of biases in LLMs, aiming to provide an extensive review of the types, sources, impacts, and mitigation strategies related to these biases. We systematically categorize biases into several dimensions. Our survey synthesizes current research findings and discusses the implications of biases in real-world applications. Additionally, we critically assess existing bias mitigation techniques and propose future research directions to enhance fairness and equity in LLMs. This survey serves as a foundational resource for researchers, practitioners, and policymakers concerned with addressing and understanding biases in LLMs.