1 citations · 1 across the 9 of their papers we have counts for
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Attributional Safety Failures in Large Language Models under Code-Mixed Perturbations
Somnath Banerjee, Pratyush Chatterjee, Shanu Kumar +4
While LLMs appear robustly safety-aligned in English, we uncover a catastrophic, overlooked weakness: attributional collapse under code-mixed perturbations. Our systematic evaluati…
Towards Safer Pretraining: Analyzing and Filtering Harmful Content in Webscale datasets for Responsible LLMs
Sai Krishna Mendu, Harish Yenala, Aditi Gulati +2
Large language models (LLMs) have become integral to various real-world applications, leveraging massive, web-sourced datasets like Common Crawl, C4, and FineWeb for pretraining. W…
SAGE: A Generic Framework for LLM Safety Evaluation
Madhur Jindal, Hari Shrawgi, Parag Agrawal +1
As Large Language Models are rapidly deployed across diverse applications from healthcare to financial advice, safety evaluation struggles to keep pace. Current benchmarks focus on…
LLM Safety for Children
Prasanjit Rath, Hari Shrawgi, Parag Agrawal +1
This paper analyzes the safety of Large Language Models (LLMs) in interactions with children below age of 18 years. Despite the transformative applications of LLMs in various aspec…