InstructEdit: Instruction-based Knowledge Editing for Large Language Models (2402.16123v2)

Abstract: Knowledge editing for LLMs can offer an efficient solution to alter a model's behavior without negatively impacting the overall performance. However, the current approaches encounter issues with limited generalizability across tasks, necessitating one distinct editor for each task, significantly hindering the broader applications. To address this, we take the first step to analyze the multi-task generalization issue in knowledge editing. Specifically, we develop an instruction-based editing technique, termed InstructEdit, which facilitates the editor's adaptation to various task performances simultaneously using simple instructions. With only one unified editor for each LLM, we empirically demonstrate that InstructEdit can improve the editor's control, leading to an average 14.86% increase in Reliability in multi-task editing setting. Furthermore, experiments involving holdout unseen task illustrate that InstructEdit consistently surpass previous strong baselines. To further investigate the underlying mechanisms of instruction-based knowledge editing, we analyze the principal components of the editing gradient directions, which unveils that instructions can help control optimization direction with stronger OOD generalization. Code and datasets are available in https://github.com/zjunlp/EasyEdit.

• The paper introduces InstructEdit, an instruction-based editor that achieves a 14.86% reliability gain in multi-task LLM editing.
• It employs meta-learning with task instructions, leading to a 42.04% improvement in handling out-of-domain data.
• The study evaluates InstructEdit on four datasets across GPT2-XL and LLaMA-2-Base, demonstrating enhanced scalability and control in knowledge editing.

InstructEdit: A New Approach to Knowledge Editing in LLMs Through Instruction-Based Methods

Introduction

In recent research conducted by Bozhong Tian, Siyuan Cheng, Xiaozhuan Liang, Ningyu Zhang, and their colleagues, a novel technique called InstructEdit has been developed to improve knowledge editing in LLMs. This method aims to address the limitations of current knowledge editing strategies, which struggle to generalize across multiple tasks and require a distinct editor for each task. By adopting an instruction-based approach, InstructEdit enables a unified editor for each LLM, resulting in enhanced control and a noticeable increase in reliability during multi-task editing sessions.

Methodology

Instruction-Based Editing Technique

The core innovation behind InstructEdit is its reliance on simple instructions to adapt the editor to various tasks. This instruction-based knowledge editing method significantly contrasts with previous task-specific approaches by offering a unified solution capable of handling multiple editing tasks concurrently.

Unified Editor Learning with Instructions

The paper explores the complex dynamics of how instructions direct the editing process. By integrating meta-learning editing methods with carefully designed instructions for training on varied tasks, InstructEdit not only outperforms single-task editors in multi-task settings but also showcases remarkable improvements in task generalization capabilities.

Experimental Setup

Datasets and Settings

Experiments were carried out on four datasets - CounterFact, Recent, ConvSent, and ZsRE - using two different scales of LLMs: GPT2-XL and LLaMA-2-Base. The paper meticulously compares InstructEdit against several baselines encompassing both paradigms of preserving and modifying models' parameters. A range of evaluation metrics, including Reliability, Generalization, Locality, and Portability, were applied to assess the performance comprehensively.

Findings and Implications

Enhanced Multi-Task Editing Capability

InstructEdit demonstrated a significant a14.86% improvement in reliability over the standard Multi-Edit approach when tested in a multi-task editing environment. This highlights the method's remarkable ability to efficiently manage edits across multiple tasks without the need for re-training or multiple editors.

Strong Out-of-Domain Generalization

Another notable finding is InstructEdit's superior performance in handling out-of-domain (OOD) data, with a 42.04% improvement seen on unseen tasks during training. This strong generalization capability suggests that InstructEdit could significantly reduce the need for continual re-training of LLMs as new tasks or knowledge domains emerge.

Future Directions

Towards More Naturalistic Instructions

The current iteration of InstructEdit utilizes task descriptions as instructions rather than natural language commands. Future work could explore refining these instructions to be more intuitive and human-like, potentially broadening the method's applicability and ease of use.

Scalability and Task Diversity

While InstructEdit exhibits impressive performance, there is room to investigate its scalability and effectiveness across a wider array of tasks, especially those involving cross-linguistic elements or substantially different knowledge domains.

Conclusion

The InstructEdit method presents a promising avenue for knowledge editing in LLMs, offering a unified, instruction-based approach to multi-task editing. By enabling strong adaptability and generalization across tasks, this approach not only enhances the efficiency of knowledge editing but also reduces the computational and resource-related burdens associated with maintaining multiple task-specific editors. As the landscape of generative AI and LLMs continues to evolve, the techniques developed in this paper could play a pivotal role in facilitating more versatile and robust LLMs.