This AI article presents a comprehensive study on knowledge editing for large language models

Recently, GPT-4 and other large language models (LLMs) have demonstrated an impressive natural language processing (NLP) ability to memorize large amounts of information, possibly even more than humans. The success of LLMs in handling massive amounts of data has led to the development of generative process models that are shorter, more coherent, and more interpretable—a “world model,” so to speak.

Additional insights are gained from LLMs' ability to understand and control complex strategic contexts; For example, previous research has shown that transformers trained to predict the next tile in board games like Othello create detailed models of the current state of the game. Researchers have discovered the ability of LLMs to learn representations that reflect perceptual and symbolic notions and track subjects' Boolean states within certain situations. With this dual capability, LLMs can store massive amounts of data and organize them in ways that mimic human thought processes, making them ideal knowledge bases.

Factual fallacies, the possibility of creating harmful content, and outdated information are some of the limitations of LLMs due to their training limits. It will take time and money to train everyone to solve these problems. In response, in recent years there has been a proliferation of LLM-focused knowledge editing approaches, allowing for efficient on-the-fly model adjustments. Understanding how LLMs display and process information is critical to ensuring the fairness and security of artificial intelligence (ai) systems; This technique focuses on specific areas of change without affecting overall performance. The main objective of this work is to study the history and current state of knowledge editing for LLMs.

New research by a team of researchers from Zhejiang University, the National University of Singapore, the University of California, Ant Group and Alibaba Group provides the initial step in providing an overview of the design of Transformers, the way in which LLMs store related knowledge and approaches. such as efficient parameter tuning, knowledge growth, continuous learning, and machine unlearning. After that, the team lays the groundwork, officially defining the knowledge editing problem, and providing a new taxonomy that brings together theories from education and cognitive science to offer a coherent perspective on knowledge editing techniques. In particular, they classify knowledge editing strategies for LLM as follows: editing internal knowledge methods, merging knowledge into the model, and resorting to external knowledge.

The researchers present their classification criteria in their article as follows:

• Leverage information from other sources: This method is analogous to the recognition phase of human cognition, which, after the initial encounter with new information, requires exposure to the information within an appropriate context.
• Integrating experiential data into the model: By drawing parallels between incoming information and current knowledge in the model, this method is similar to the association phase in human cognitive processes. A representation of the learned knowledge would be combined or used in place of the outcome or intermediate result of the methods.
• Reviewing inherent information: Reviewing knowledge in this way is similar to going through the “mastery phase” of learning something new. It implies that the model constantly uses LLM weight modifications to incorporate knowledge into its parameters.

Subsequently, twelve sets of natural language processing data are subjected to extensive experiments in this paper. Performance, usability, underlying mechanisms, and other issues are carefully considered in its design.

To provide a fair comparison and show how well these methods perform in information insertion, modification, and deletion environments, the researchers build a new benchmark called KnowEdit and describe empirical results of state-of-the-art LLM knowledge editing techniques. generation.

Researchers demonstrate how knowledge editing affects both general task and multitasking knowledge editing, suggesting that modern knowledge editing methods successfully update facts with little impact on cognitive abilities and model adaptability. in different domains of knowledge. In modified LLMs, they find that one or more columns in the value layer are very focused. It has been suggested that LLMs can retrieve responses by retrieving information from their pre-training corpus or through a multi-step reasoning process.

The findings suggest that knowledge localization processes, such as causal analysis, focus on areas related to the entity in question and not on the entire factual context. Additionally, the team is also exploring the potential for knowledge editing for LLMs to have unforeseen implications, which is an important element to think about carefully.

Finally, they explore the wide range of uses of knowledge editing, analyzing its possibilities from various angles. These uses include trustworthy ai, efficient machine learning, ai generated content (AIGC), and individualized agents in human-computer interaction. The researchers hope that this study can generate new lines of research on LLMs with an eye to efficiency and creativity. They have released all of their resources (including codes, data splits, and trained model checkpoints) to the public to facilitate and inspire further studies.

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