1 citations · 1 across the 5 of their papers we have counts for
5 papers
SHARD: Safe and Helpful Alignment via Self-Reframing Distillation
Viswonathan Manoranjan, Amogh Gupta, Anvesh Rao Vijjini +2
Large language models often struggle with sensitive prompts. They may refuse outright, provide generic safety boilerplate, or fail to address the user's legitimate informational ne…
Do LLM Agents Mirror Socio-Cognitive Effects in Power-Asymmetric Conversations?
Anvesh Rao Vijjini, Sagar Manjunath, Snigdha Chaturvedi
Power differences shape human communication through well documented socio cognitive effects, including language coordination, pronoun usage, authority bias, and harmful compliance.…
PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory
Bowen Jiang, Yuan Yuan, Maohao Shen +13
Personalization is one of the next milestones in advancing AI capability and alignment. We introduce PersonaMem-v2, the state-of-the-art dataset for LLM personalization that simula…
SocialGaze: Improving the Integration of Human Social Norms in Large Language Models
Anvesh Rao Vijjini, Rakesh R. Menon, Jiayi Fu +2
While much research has explored enhancing the reasoning capabilities of large language models (LLMs) in the last few years, there is a gap in understanding the alignment of these…
Exploring Safety-Utility Trade-Offs in Personalized Language Models
Anvesh Rao Vijjini, Somnath Basu Roy Chowdhury, Snigdha Chaturvedi
As large language models (LLMs) become increasingly integrated into daily applications, it is essential to ensure they operate fairly across diverse user demographics. In this work…