22 citations · 22 across the 4 of their papers we have counts for
5 papers
How User-AI Mistreatment Occurs and Matters in Conversational Systems?
Fanqi Zeng, Sadid A. Hasan, Chaocheng He
Safety research often focuses on model-generated harms, but users may also direct hostility, coercion, and adversarial pressure at models. Understanding how and when that occurs is…
Can LLMs Truly Forget? Revealing Unlearning Gaps Through Adversarial Evaluation
Ayush Gupta, Hima Varshini Surisetty, Sreevidya Bollineni +5
Machine unlearning aims to remove the influence of targeted training data from a model while preserving its remaining capabilities, but evaluating whether such information has trul…
Auto-Eval Judge: Towards a General Agentic Framework for Task Completion Evaluation
Roshita Bhonsle, Rishav Dutta, Sneha Vavilapalli +8
The increasing adoption of foundation models as agents across diverse domains necessitates a robust evaluation framework. Current methods, such as LLM-as-a-Judge, focus only on fin…
Does Prompt Formatting Have Any Impact on LLM Performance?
Jia He, Mukund Rungta, David Koleczek +3
In the realm of Large Language Models (LLMs), prompt optimization is crucial for model performance. Although previous research has explored aspects like rephrasing prompt contexts,…
Alternate Preference Optimization for Unlearning Factual Knowledge in Large Language Models
Anmol Mekala, Vineeth Dorna, Shreya Dubey +5
Machine unlearning aims to efficiently eliminate the influence of specific training data, known as the forget set, from the model. However, existing unlearning methods for Large La…