Papers by Aditya Vashistha
“#DisabledOnIndianTwitter” : A Dataset towards Understanding the Expression of People with Disabilities on Indian Twitter (2022.findings-aacl)
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| Challenge: | a majority of disabled Indians exist at the margins of society with little to no access to social media . as access to ICTs and high-speed internet grows, Indian Twitter's user base is expanding to include disability influencers, activists, and everyday disabled users. |
| Approach: | They propose a hierarchical annotation taxonomy to classify tweets into various themes including discrimination, advocacy, and self-identification. |
| Outcome: | The proposed taxonomy classifies 2,384 tweets into various themes including discrimination, advocacy, and self-identification. |
SMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text Generation (2025.naacl-long)
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| Challenge: | a scalable approach to classify text with sensitivity is costly because of exponential time complexity. |
| Approach: | They propose a framework for calculating word-level local and global sensitivities . they use a CHECKLIST-generated sentiment analysis dataset to test their approach . |
| Outcome: | The proposed framework can be used to calculate word-level local and global sensitivities . it improves attacks by 15.58%, while using sensitivity as an additional reward improves . |
Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks (2024.lrec-main)
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| Challenge: | Existing methods to jailbreak large language models have been poorly studied . a recent study showed that non-expert users can jailbreak LLMs by manipulating their prompts . |
| Approach: | They propose a formalism and a taxonomy of known (and possible) jailbreaks . they propose generating a dataset of model outputs across 3700 jailbreak prompts a 'prompt' attack is a new attack popularly categorized as "prompting injection attacks" |
| Outcome: | The proposed model exploits 3700 jailbreak prompts over 4 tasks to analyze their effectiveness . authors show that the model can learn to perform a new task on unseen examples . |