Papers by Sahil Manchanda
Uncovering Currency Bias and Syntax Gap in Text Embedding Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Text-embedding models often inherit societal biases, yet the influence of socio-economic markers remains unexplored. |
| Approach: | They propose to identify Currency Bias as a systemic representational limitation in financial AI . they analyze currency embeddings to identify currency identifiers and associative sensitivity . |
| Outcome: | The proposed model lacks associative sensitivity to economic hierarchies, the authors show . they show that current embedding practices pose significant risks for the fairness and reliability of financial NLP applications. |
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. |