Papers by Aniket Vashishtha
On Evaluating and Mitigating Gender Biases in Multilingual Settings (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks and resources for evaluating gender biases in multilingual settings are limited. |
| Approach: | They propose to extend DisCo to different Indian languages using human annotations to evaluate gender biases in multilingual models. |
| Outcome: | The proposed benchmarks and mitigation techniques are extended beyond English to evaluate gender biases in multilingual models. |
Performance and Risk Trade-offs for Multi-word Text Prediction at Scale (2023.findings-eacl)
Copied to clipboard
Aniket Vashishtha, S Sai Prasad, Payal Bajaj, Vishrav Chaudhary, Kate Cook, Sandipan Dandapat, Sunayana Sitaram, Monojit Choudhury
| Challenge: | Large Language Models (LLMs) generate ethically inappropriate texts even for seemingly innocuous contexts. |
| Approach: | They propose to use large language models to detect and filter toxic content in text prediction tasks by evaluating their toxicity detection approaches against a manually crafted CheckList of harms. |
| Outcome: | The proposed methods are compared against a checklist of harms targeted at different groups and different levels of severity in English. |