Papers by Abhinav Java
“Thinking” Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models (2024.emnlp-main)
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Shaz Furniturewala, Surgan Jandial, Abhinav Java, Pragyan Banerjee, Simra Shahid, Sumit Bhatia, Kokil Jaidka
| Challenge: | Existing debiasing techniques are typically training-based or require access to the model’s internals and output distributions, so they are inaccessible to end-users looking to adapt LLM outputs for their particular needs. |
| Approach: | They propose a system-based iterative framework that uses System 2 thinking processes to induce logical, reflective, and critical text generation with single, multi-step, instruction, and role-based variants. |
| Outcome: | The proposed framework significantly improves over other frameworks demonstrating lower mean bias in the outputs with competitive performance on the downstream tasks. |
Towards Operationalizing Right to Data Protection (2025.naacl-long)
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| Challenge: | Recent work introduces the concept of generating unlearnable datasets (by adding imperceptible spurious correlations to the clean data) this approach is limited by several practical constraints like requiring knowledge of the target model. |
| Approach: | They propose a framework that injects imperceptible spurious correlations into natural language datasets, rendering them unlearnable without affecting semantic content. |
| Outcome: | The proposed framework can restrict newer models like GPT-4o and Llama from learning on generated data, resulting in a drop in test accuracy compared to their zero-shot performance. |