5 citations · 8 across the 12 of their papers we have counts for
4 papers · 1 filter
Investigating Implicit Bias in Large Language Models: A Large-Scale Study of Over 50 LLMs
Divyanshu Kumar, Umang Jain, Sahil Agarwal +1
Large Language Models (LLMs) are being adopted across a wide range of tasks, including decision-making processes in industries where bias in AI systems is a significant concern. Re…
VERA: Validation and Enhancement for Retrieval Augmented systems
Nitin Aravind Birur, Tanay Baswa, Divyanshu Kumar +3
Large language models (LLMs) exhibit remarkable capabilities but often produce inaccurate responses, as they rely solely on their embedded knowledge. Retrieval-Augmented Generation…
SAGE-RT: Synthetic Alignment data Generation for Safety Evaluation and Red Teaming
Anurakt Kumar, Divyanshu Kumar, Jatan Loya +4
We introduce Synthetic Alignment data Generation for Safety Evaluation and Red Teaming (SAGE-RT or SAGE) a novel pipeline for generating synthetic alignment and red-teaming data. E…
Fine-Tuning, Quantization, and LLMs: Navigating Unintended Outcomes
Divyanshu Kumar, Anurakt Kumar, Sahil Agarwal +1
Large Language Models (LLMs) have gained widespread adoption across various domains, including chatbots and auto-task completion agents. However, these models are susceptible to sa…