3 citations · 4 across the 9 of their papers we have counts for
4 papers · 1 filter
Distribution Corrected Offline Data Distillation for Large Language Models
Yumeng Zhang, Zhengbang Yang, Yevin Nikhel Goonatilake +1
Distilling reasoning traces from strong large language models into smaller ones is a promising route to improve intelligence in resource-constrained settings. Existing approaches f…
Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification
Zhengbang Yang, Md. Tasin Tazwar, Minghan Wei +1
Robotic path planning problems are often NP-hard, and practical solutions typically rely on approximation algorithms with provable performance guarantees for general cases. While d…
DUET: Distilled LLM Unlearning from an Efficiently Contextualized Teacher
Yisheng Zhong, Zhengbang Yang, Zhuangdi Zhu
LLM unlearning is a technique to remove the impacts of undesirable knowledge from the model without retraining from scratch, which is indispensable towards trustworthy AI. Existing…
CALIBURN: Self-Calibrated LLM Unlearning Alignment
Zhengbang Yang, Yisheng Zhong, Junyuan Hong +1
LLM unlearning aims to remove the influence of undesirable knowledge from pretrained language models, which offers a practical mechanism for addressing safety and privacy concerns.…