1 citations · 1 across the 9 of their papers we have counts for
9 papers
CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts
Shanu Kumar, Shubhanshu Khandelwal, Akhila Yesantarao Venkata +3
Prompts tuned for accuracy often grow long, raising inference cost on every model call. The best accuracy-cost trade-off depends on the task and the budget, so prompt optimization…
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…
Socio-Culturally Aware Evaluation Framework for LLM-Based Content Moderation
Shanu Kumar, Gauri Kholkar, Saish Mendke +3
With the growth of social media and large language models, content moderation has become crucial. Many existing datasets lack adequate representation of different groups, resulting…
Enhancing Zero-shot Chain of Thought Prompting via Uncertainty-Guided Strategy Selection
Shanu Kumar, Saish Mendke, Karody Lubna Abdul Rahman +3
Chain-of-thought (CoT) prompting has significantly enhanced the capability of large language models (LLMs) by structuring their reasoning processes. However, existing methods face…
SCULPT: Systematic Tuning of Long Prompts
Shanu Kumar, Akhila Yesantarao Venkata, Shubhanshu Khandelwal +3
Prompt optimization is essential for effective utilization of large language models (LLMs) across diverse tasks. While existing optimization methods are effective in optimizing sho…
Navigating the Cultural Kaleidoscope: A Hitchhiker's Guide to Sensitivity in Large Language Models
Somnath Banerjee, Sayan Layek, Hari Shrawgi +7
As LLMs are increasingly deployed in global applications, the importance of cultural sensitivity becomes paramount, ensuring that users from diverse backgrounds feel respected and…