Papers by Shachi Dave
SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models (2023.acl-long)
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Akshita Jha, Aida Mostafazadeh Davani, Chandan K Reddy, Shachi Dave, Vinodkumar Prabhakaran, Sunipa Dev
| Challenge: | Existing datasets on social stereotypes are limited in size and coverage . existing datasets are restricted to stereotypes prevalent in the Western society . |
| Approach: | They propose a broad-coverage stereotype dataset using generative models and a globally diverse rater pool to validate the prevalence of stereotypes in society. |
| Outcome: | The dataset validates the prevalence of stereotypes in society across 8 geo-political regions across 6 continents and states within the US and India. |
Parameter-Efficient Finetuning for Robust Continual Multilingual Learning (2023.findings-acl)
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| Challenge: | Existing approaches to Continual Multilingual Learning (CML) are based on updating models using new data in stages. |
| Approach: | They propose a parameter-efficient finetuning strategy to increase the number of languages on which the model improves after an update while reducing the magnitude of loss for the remaining languages. |
| Outcome: | The proposed model improves on the languages included in the latest update while reducing the loss of performance on the remaining languages. |
Re-contextualizing Fairness in NLP: The Case of India (2022.aacl-main)
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| Challenge: | Recent research has revealed undesirable biases in NLP data and models . however, these efforts focus of social disparities in the West and are not directly portable to other geo-cultural contexts. |
| Approach: | They propose a framework to re-contextualize NLP fairness research for the Indian context . they build resources for fairness evaluation in the Indian and delve deeper into social stereotypes for Region and Religion . |
| Outcome: | The proposed framework can be generalized to other geo-cultural contexts. |
ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation (2024.acl-long)
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Akshita Jha, Vinodkumar Prabhakaran, Remi Denton, Sarah Laszlo, Shachi Dave, Rida Qadri, Chandan Reddy, Sunipa Dev
| Challenge: | Existing approaches for evaluating stereotypes have a noticeable lack of coverage of global identity groups and their associated stereotypes. |
| Approach: | They propose to use a dataset to evaluate nationality-based stereotypes in T2I models across 135 nationalities to assess offensive stereotypes. |
| Outcome: | The proposed dataset enables evaluation of known nationality-based stereotypes across 135 nationalities. |
Bootstrapping Multilingual Semantic Parsers using Large Language Models (2023.eacl-main)
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| Challenge: | Despite cross-lingual generalization, translation models require significant amounts of labeled data for many low-resource languages . brittle translation services may be due to domain mismatch between input text and general-purpose text . |
| Approach: | They propose to use large language models to translate English datasets into several languages via few-shot prompting. |
| Outcome: | The proposed method outperforms a strong translation-train baseline on 41 out of 50 languages. |