Instruction-Based Knowledge Editing for Improving Generalization of Large Language Models

Keskeiset käsitteet

Instruction-based editing can enhance the multi-task generalization capabilities of knowledge editing methods for large language models.

Tiivistelmä

The paper proposes an instruction-based knowledge editing technique, termed InstructEdit, to address the multi-task generalization issue in knowledge editing for large language models (LLMs).

Key highlights:

• Current knowledge editing approaches often struggle with limited generalizability across tasks, requiring distinct editors for each task.
• InstructEdit utilizes instructions to guide the editing process, enabling a unified editor to handle multiple tasks simultaneously.
• Experiments show that InstructEdit can improve the editor's reliability by 14.86% on average compared to previous methods in a multi-task setting.
• InstructEdit also demonstrates superior performance on unseen tasks, outperforming strong baselines by 42.04%.
• Analysis reveals that instructions help control the optimization direction of the editing gradient, leading to better out-of-distribution generalization.
• Appropriate data proportions across tasks are crucial for effective multi-task editing.

The paper takes a significant step towards developing more generalizable knowledge editing techniques for LLMs by leveraging instruction-based guidance.

Tilastot

InstructEdit can improve the editor's reliability by 14.86% on average compared to previous methods in a multi-task setting.
InstructEdit outperforms strong baselines by 42.04% on unseen tasks.

Lainaukset

"Instruction Tuning can enhance the LLMs' comprehension skills by providing clearer commands or instructions, enabling the model to understand better and execute accurate responses."
"Inspired by the generalization capabilities of Instruction Tuning, we take the first step to integrate instructions into knowledge editing for LLMs, endowing one unified Editor with commendable instruction generalization and zero-shot capabilities to concurrently handle multiple editing tasks."

Tärkeimmät oivallukset

InstructEdit: Instruction-based Knowledge Editing for Large Language Models

by Ningyu Zhang... klo arxiv.org 04-30-2024

https://arxiv.org/pdf/2402.16123.pdf

Syvällisempiä Kysymyksiä

How can the instruction-based editing approach be extended to handle cross-lingual or cross-modal knowledge editing tasks?

The instruction-based editing approach can be extended to handle cross-lingual or cross-modal knowledge editing tasks by incorporating language-specific or modality-specific instructions into the training process. For cross-lingual tasks, instructions can be generated in multiple languages to guide the editing process for models that need to understand and edit knowledge across different languages. This can involve training the editor on multilingual datasets and providing instructions in each language to ensure accurate editing in diverse linguistic contexts. Additionally, for cross-modal tasks, instructions can be tailored to different modalities such as text, images, or audio, enabling the model to edit knowledge across various types of data. By incorporating modality-specific instructions, the editor can learn to effectively manipulate knowledge in different modalities, enhancing its versatility and applicability in cross-modal editing tasks.

What are the potential limitations of the instruction-based editing approach, and how can they be addressed?

One potential limitation of the instruction-based editing approach is the reliance on the quality and specificity of the instructions provided. If the instructions are vague or ambiguous, the editor may struggle to accurately interpret and implement the desired edits. To address this limitation, it is essential to improve the instruction generation process by incorporating more detailed and informative instructions. This can involve leveraging advanced natural language processing techniques to generate precise and contextually relevant instructions that guide the editing process effectively. Additionally, incorporating feedback mechanisms to iteratively refine and optimize the instructions based on the editor's performance can help enhance the quality of the instructions and mitigate potential limitations.

How can the instruction generation process be further improved to provide more informative and effective guidance for the editing process?

The instruction generation process can be further improved to provide more informative and effective guidance for the editing process by incorporating domain-specific knowledge and context into the instructions. This can involve leveraging domain expertise to craft instructions that are tailored to the specific editing tasks and datasets, ensuring that the editor receives relevant and actionable guidance. Additionally, integrating interactive interfaces or human-in-the-loop systems can enable users to provide real-time feedback on the generated instructions, allowing for continuous refinement and improvement. Furthermore, exploring advanced techniques such as reinforcement learning or adversarial training to optimize the instruction generation process based on the editor's performance can enhance the quality and effectiveness of the instructions for knowledge editing tasks.