EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models

Abstract: In recent years, instruction tuning has gained increasing attention and emerged as a crucial technique to enhance the capabilities of LLMs. To construct high-quality instruction datasets, many instruction processing approaches have been proposed, aiming to achieve a delicate balance between data quantity and data quality. Nevertheless, due to inconsistencies that persist among various instruction processing methods, there is no standard open-source instruction processing implementation framework available for the community, which hinders practitioners from further developing and advancing. To facilitate instruction processing research and development, we present EasyInstruct, an easy-to-use instruction processing framework for LLMs, which modularizes instruction generation, selection, and prompting, while also considering their combination and interaction. EasyInstruct is publicly released and actively maintained at https://github.com/zjunlp/EasyInstruct, along with an online demo app and a demo video for quick-start, calling for broader research centered on instruction data and synthetic data.

• The paper introduces EasyInstruct, a modular framework that standardizes instruction processing for LLMs through unified generation, selection, and prompting.
• The framework integrates diverse data sources such as chat data, corpus, and knowledge graphs to generate high-quality instructions and improve model performance.
• EasyInstruct offers flexible deployment with zero-code, low-code, and advanced customization options, making LLM tuning accessible to researchers and practitioners.

Overview of "EasyInstruct: An Easy-to-use Instruction Processing Framework for LLMs"

Introduction

The paper introduces EasyInstruct, an instruction processing framework tailored for LLMs. As instruction tuning is pivotal in optimizing LLM performance, EasyInstruct addresses the need for a standardized, modular approach to instruction processing. Despite various proposed methods for instruction set creation, inconsistencies exist, often complicating broader research and application in the field. EasyInstruct aims to streamline this by modularizing components such as instruction generation, selection, and prompting, while also enabling their integration.

Design and Implementation

Design Principles

EasyInstruct is structured to cater to a spectrum of users, from novices to experts, by offering:

• Zero-Code Instruction Processing: Non-programmers can utilize predefined configurations and scripts for a code-free experience.
• Low-Code Customization: Intermediate users can modify inputs and outputs with minimal coding.
• Advanced Component Extension: Experienced users can extend and customize base classes to suit specific needs.

Major Components

1. APIs and Engines: The framework integrates with major LLM services like OpenAI and Cohere, and supports execution on open-source LLMs like LLaMA, ensuring robust instruction execution processes.
2. Generators: This module facilitates automated instruction generation, leveraging different seed sources:
   • Chat Data: Implements methods like Self-Instruct, allowing generation of new instructions from seed tasks.
   • Corpus: Methods like Backtranslation predict instructions based on corpus paragraphs.
   • Knowledge Graphs: Generates instruction datasets for Information Extraction tasks, merging existing knowledge graphs with instruction templates.
3. Selectors: Central to curating high-quality instruction datasets, this module evaluates instructions using various metrics, such as lexical diversity and model-based scoring.
4. Prompts: This component standardizes instruction prompting, ensuring consistent interaction with LLMs. It supports techniques such as Chain-of-Thought and Information Extraction.

Evaluation

The framework's efficacy was tested by creating and refining several datasets using EasystInstruct’s capabilities:

• Datasets like self_instruct_5k and easyinstruct_5k demonstrated significant improvements in model performance during evaluation on AlpacaFarm.
• The study of dataset diversity illustrated a broad range of verb-noun structures in instructions, indicating high variability and richness.
• A qualitative case study showed a selection of high-quality, logically coherent instructions.

Implications and Future Directions

EasyInstruct, by combining instruction generation, selection, and prompting into a cohesive framework, accelerates the development of LLM applications. It enhances the quality and efficiency of instruction data synthesis, offering significant practical benefits in fine-tuning LLMs across various domains.

The framework’s open-source nature encourages continuous improvements and extensions. Future developments could include enhancements for knowledge-intense data generation, further broadening its applicability. Additionally, as LLMs evolve, EasyInstruct could integrate advancements in multimodal instruction processing.

In conclusion, EasyInstruct equips researchers and practitioners with a powerful tool for effective instruction processing, paving the way for more sophisticated and refined uses of LLMs. Predictably, its adoption will spur further innovations and applications within the AI community.