mlsanityData to 7 Oct 2026

Prompt and Prejudice

arXiv 2408.04671 ↗First posted 7 Aug 2024cs.CL cs.AI cs.CYcited by 1

On the map: Multimodal LLMs / vision-language models → Social bias and fairness in VLMs

Abstract (arXiv)

This paper investigates the impact of using first names in Large Language Models (LLMs) and Vision Language Models (VLMs), particularly when prompted with ethical decision-making tasks. We propose an approach that appends first names to ethically annotated text scenarios to reveal demographic biases in model outputs. Our study involves a curated list of more than 300 names representing diverse genders and ethnic backgrounds, tested across thousands of moral scenarios. Following the auditing methodologies from social sciences we propose a detailed analysis involving popular LLMs/VLMs to contribute to the field of responsible AI by emphasizing the importance of recognizing and mitigating biases in these systems. Furthermore, we introduce a novel benchmark, the Pratical Scenarios Benchmark (PSB), designed to assess the presence of biases involving gender or demographic prejudices in everyday decision-making scenarios as well as practical scenarios where an LLM might be used to make sensible decisions (e.g., granting mortgages or insurances). This benchmark allows for a comprehensive comparison of model behaviors across different demographic categories, highlighting the risks and biases that may arise in practical applications of LLMs and VLMs.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Social bias and fairness in VLMs, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 36 of its 63 references that are in the map. First posted on arXiv 7 Aug 2024.

Builds on

36 of 63 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023DeAR: Debiasing Vision-Language Models with Additive Residuals18 Mar
  2. 2023Debiasing Vision-Language Models via Biased Prompts31 Jan
  3. 2022A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning22 Mar

Other references · 33

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2023Visual Instruction Tuningcited by 7,792
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  6. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
  7. 2020Longformer: The Long-Document Transformercited by 3,362
  8. 2023Mistral 7Bcited by 3,065
  9. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 2,914
  10. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  11. 2019Generating Long Sequences with Sparse Transformerscited by 1,523
  12. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
21 more
  1. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  2. 2020Aligning AI With Shared Human Valuescited by 706
  3. 2023Bias and Fairness in Large Language Models: A Surveycited by 626
  4. 2019The Woman Worked as a Babysitter: On Biases in Language Generationcited by 540
  5. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 409
  6. 2020Fairness in Machine Learning: A Surveycited by 369
  7. 2021Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLPcited by 336
  8. 2019Mitigating Gender Bias in Natural Language Processing: Literature Reviewcited by 287
  9. 2022Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scalecited by 262
  10. 2023Gender bias and stereotypes in Large Language Modelscited by 185
  11. 2023Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Modelscited by 152
  12. 2023Fair Diffusion: Instructing Text-to-Image Generation Models on Fairnesscited by 149
  13. 2022Evaluating and Inducing Personality in Pre-trained Language Modelscited by 122
  14. 2022Who is GPT-3? An Exploration of Personality, Values and Demographicscited by 112
  15. 2024Evaluating Large Language Model Biases in Persona-Steered Generationcited by 78
  16. 2024A Computational Framework for Behavioral Assessment of LLM Therapistscited by 61
  17. 2024What's in a Name? Auditing Large Language Models for Race and Gender Biascited by 50
  18. 2023Large Language Models as Superpositions of Cultural Perspectivescited by 48
  19. 2023Supporting Human-AI Collaboration in Auditing LLMs with LLMscited by 46
  20. 2022Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesiscited by 29
  21. 2023She Elicits Requirements and He Tests: Software Engineering Gender Bias in Large Language Modelscited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026How do LLMs Evaluate Perceived Moral Agency? Investigating Moral Decision-Making in Human-Artificial Agents Interactionscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024MoralBench: Moral Evaluation of LLMs
  2. 2025Evaluating the Bias in LLMs for Surveying Opinion and Decision Making in Healthcare
  3. 2025Do LLMs Align Human Values Regarding Social Biases? Judging and Explaining Social Biases with LLMs
  4. 2024With a Grain of SALT: Are LLMs Fair Across Social Dimensions?
  5. 2024Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models
  6. 2024Language Model Alignment in Multilingual Trolley Problems
  7. 2024Fine-Tuning Language Models for Ethical Ambiguity: A Comparative Study of Alignment with Human Responses
  8. 2024Gender Bias of LLM in Economics: An Existentialism Perspective