Summary of Gender Bias in Machine Translation and the Era Of Large Language Models, by Eva Vanmassenhove
Gender Bias in Machine Translation and The Era of Large Language Models
by Eva Vanmassenhove
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 A novel chapter explores how Machine Translation perpetuates gender bias, highlighting challenges in cross-linguistic settings and statistical dependencies. Building on existing work, this study provides a comprehensive overview of conventional Neural Machine Translation approaches and Generative Pretrained Transformer models used as Machine Translation systems. The experiment employing ChatGPT (based on GPT-3.5) in an English-Italian translation context demonstrates the system’s current capacity to address gender bias. The findings underscore the need for advancements in mitigating bias in Machine Translation systems, emphasizing fairness and inclusivity in language technologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning researchers are trying to figure out why certain translations can be sexist or unfair. They looked at how machine translation systems work and found that they can perpetuate biases from one language to another. The team used a special AI model called ChatGPT to translate text from English to Italian, and it showed that the model still has problems with gender bias. This means we need to make sure our translations are fair and don’t discriminate against certain groups. |
Keywords
* Artificial intelligence * Gpt * Machine learning * Transformer * Translation
