Summary of Multilingual Text-to-image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help You, by Felix Friedrich et al.
Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help You
by Felix Friedrich, Katharina Hämmerl, Patrick Schramowski, Manuel Brack, Jindrich Libovicky, Kristian Kersting, Alexander Fraser
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the surprising discovery that multilingual text-to-image generation models exhibit significant gender biases, similar to their monolingual counterparts. Despite the expectation of uniform results across languages, the study finds important differences between languages. The authors propose a novel benchmark, MAGBIG, to encourage research on gender bias in multilingual models and investigate the effect of multilingualism on these biases using prompts requesting portraits of individuals with specific occupations or traits. They also experiment with prompt engineering strategies to mitigate these biases, but find limited success, resulting in worse text-to-image alignment. This highlights the need for further research into diverse representations across languages and steerability to address biased model behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Text-to-image generation models are getting better at making images from text descriptions! But did you know that some of these models can be a bit unfair? They might make women look more serious or men look stronger, just because of the language they’re using. This happens even when the same text is translated into different languages. The researchers want to find out why this is happening and how we can fix it. They came up with a new way to test these models and found that some of them are worse than others at being fair. They also tried changing the way they ask for the images, but that didn’t really help either. So now we know that we need to keep working on making sure our image-making machines are fair and equal! |
Keywords
* Artificial intelligence * Alignment * Image generation * Prompt
