# Fairness, Accountability, and Transparency In Prompts

## Debiasing using prompts

1. [MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection](https://arxiv.org/abs/2305.09335)
2. [Debiasing Vision-Language Models via Biased Prompts](https://arxiv.org/abs/2302.00070)
3. [Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts](https://aclanthology.org/2022.acl-long.72.pdf)
4. [A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning](https://arxiv.org/pdf/2203.11933.pdf)
5. [Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation](https://arxiv.org/pdf/2303.15413.pdf)
6. (good) [Understanding Stereotypes in Language Models: Towards Robust Measurement and Zero-Shot Debiasing](https://arxiv.org/pdf/2212.10678.pdf)
7. [Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00434/108865)

## Hallucinations

1. (very good) [understanding LLM hallucinations](https://www.rungalileo.io/blog/deep-dive-into-llm-hallucinations-across-generative-tasks?utm_medium=email&_hsmi=304176203\&utm_content=303486713\&utm_source=hs_email)


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