Author

Snigdha Chaturvedi

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#natural language process... Jun 2026

SHARD: Safe and Helpful Alignment via Self-Reframing Distillation

SHARD is introduced, a self-reframing distillation method to improve safe-helpfulness in large language models that first rewrites sensitive prompts to surface benign intent using philosophical guidelines, then reframes its original responses into safe, more helpful ones, and finally fine-tunes the model on its self-re...

Viswonathan Manoranjan, Amogh Gupta, Anvesh Rao Vijjini et al. · arXiv.org · 0 citations

#natural language process... Conference Open access May 2026

Do LLM Agents Mirror Socio-Cognitive Effects in Power-Asymmetric Conversations?

This work examines whether large language models exhibit similar behaviors when assigned high or low status personas, and shows that LLMs show key socio-cognitive effects of power, albeit with nuances and variability, linking simulated interactions to both desirable and unsafe behaviors.

Anvesh Rao Vijjini, S. Manjunath, Snigdha Chaturvedi · Annual Meeting of the Associ... · 0 citations

#artificial intelligence Conference Open access Jun 2026

CASPER in the Machine: Insights into Character Variety in LLM-Generated Stories

This work borrows definitions from narratology to analyze eight intricate dimensions of character, such as stylization and wholeness, which consider more than just basic characteristics of characters within LLM and human-written stories.

A. Brei, Abhisheik Sharma, Nicholas Sanaie et al. · Annual Meeting of the Associ... · 0 citations