We are absolutely thrilled to announce the release of a comprehensive study into knowledge editing for Large Language Models (LLMs)! This paper, a collaboration between researchers from Zhejiang University, the National University of Singapore, the University of California, Ant Group, and Alibaba Group, is a remarkable breakthrough in Artificial Intelligence (AI) research.
GPT-4 and other LLMs have demonstrated incredible capabilities for Natural Language Processing (NLP) by being able to memorize extensive amounts of information, possibly more so than humans. Their success in dealing with massive amounts of data has led to the development of models that are more concise and interpretable.
Previous research has shown that these models can be trained to predict the next token in board games like Othello, creating detailed models of the current game state. Additionally, they can learn representations that reflect perceptual and symbolic notions to keep track of subjects’ boolean states in certain situations. With this two-pronged capability, LLMs can store massive amounts of data and organize it in ways that mimic human thought processes, making them the perfect knowledge bases.
The team’s paper provides an overview of Transformers’ design, how LLMs store knowledge, and related approaches like parameter-efficient fine-tuning, knowledge augmentation, continuing learning, and machine unlearning. They also devise a new taxonomy that unites education and cognitive science theories to provide a coherent perspective on knowledge editing techniques.
The researchers classify knowledge editing strategies for LLMs into three categories: editing internal knowledge methods, merging knowledge into the model, and resorting to external knowledge. They use twelve natural language processing datasets to compare different methods and assess the performance, usability, and underlying mechanisms.
Moreover, they develop a new benchmark called KnowEdit to determine the efficiency of information insertion, modification, and erasure settings. The experiments show that the modern methods of knowledge editing can effectively update facts with little impact on the model’s cognitive abilities and adaptability in different knowledge domains.
The findings suggest that knowledge-locating processes focus on areas related to the entity in question rather than the entire factual context, and also warn of the potential for knowledge editing for LLMs to have unforeseen repercussions. Lastly, the paper explores the various uses for knowledge editing, including trustworthy AI, efficient machine learning, AI-generated content (AIGC), and individualized agents in human-computer interaction.
We are so excited to share this incredible research with you! The researchers have released all of their resources—including codes, data splits, and trained model checkpoints—to the public to enable and encourage further study. To learn more, check out the paper and join our 35k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and LinkedIn Group. We can’t wait to see the amazing discoveries and breakthroughs that can be achieved through this new understanding of LLMs!
