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Zhoubo Li

2 accepted papers

2024

Unveiling the Pitfalls of Knowledge Editing for Large Language Models

ICLR 2024poster

As the cost associated with fine-tuning Large Language Models (LLMs) continues to rise, recent research efforts have pivoted towards developing methodologies to edit implicit knowledge embedded within LLMs. Yet, there's still a dark cloud lingering overhead -- will knowledge editing trigger butterfl…

2023

Editing Large Language Models: Problems, Methods, and Opportunities

EMNLP 2023long main

Despite the ability to train capable LLMs, the methodology for maintaining their relevancy and rectifying errors remains elusive. To this end, the past few years have witnessed a surge in techniques for editing LLMs, the objective of which is to alter the behavior of LLMs \textbf{efficiently} withi…

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