Papers by Pannaga Shivaswamy
What is in a name? Mitigating Name Bias in Text Embedding Similarity via Anonymization (2025.findings-acl)
Copied to clipboard
| Challenge: | Text-embedding models often exhibit name bias due to data on which they are trained. |
| Approach: | They propose a method to mitigate name bias in text-embedding models by removing references to names from the text. |
| Outcome: | The proposed approach achieves significant performance gains on three downstream NLP tasks involving embedding similarities. |