Papers by Preethi Lahoti
AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications (2023.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) are rapidly becoming more and more popular, but dealing with the potential harms associated with their deployment in real-world scenarios is still an open research question. |
| Approach: | They propose an automated approach for automated generation of adversarial evaluation datasets to test the safety of LLM generations on new downstream applications. |
| Outcome: | AART generates evaluation datasets with high diversity of content characteristics critical for effective adversarial testing. |
Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting (2023.emnlp-main)
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Preethi Lahoti, Nicholas Blumm, Xiao Ma, Raghavendra Kotikalapudi, Sahitya Potluri, Qijun Tan, Hansa Srinivasan, Ben Packer, Ahmad Beirami, Alex Beutel, Jilin Chen
| Challenge: | Existing studies on diversity in large language models focus on the understudied class of fairness and inclusion concern in LLMs. |
| Approach: | They propose a technique to measure diversity in generated responses along people and culture axes by collective-critique and self-voting. |
| Outcome: | The proposed approach outperforms baseline methods and human evaluations with human and automated evaluations. |