A New Pipeline For Generating Instruction Dataset via RAG and Self Fine-Tuning

TL;DR

This paper introduces a pipeline that uses retrieval-augmented generation and self fine-tuning to create high-quality, domain-specific instruction datasets from limited documents, enabling effective fine-tuning of large language models for specialized fields.

Contribution

The proposed pipeline automates domain-specific dataset creation using RAG and self fine-tuning, reducing manual effort and adapting quickly to domain updates, demonstrated in the psychiatry domain.

Findings

Successfully generated instruction datasets for psychiatry.

Fine-tuned LLMs showed improved domain relevance.

Pipeline adapts to document updates without retraining.

Abstract

With the rapid development of large language models in recent years, there has been an increasing demand for domain-specific Agents that can cater to the unique needs of enterprises and organizations. Unlike general models, which strive for broad coverage, these specialized Agents rely on focused datasets tailored to their intended applications. This research proposes a pipeline that leverages the power of LLMs and the Retrieval-Augmented Generation related framework to construct high-quality instruction datasets for fine-tuning on specific domains using custom document collections. By ingesting domain-specific documents, the pipeline generates relevant and contextually appropriate instructions, thus effectively creating a comprehensive dataset for fine-tuning LLMs on the target domain. This approach overcomes the limitations of traditional dataset creation methods, which often rely on manual curation or web-scraping techniques that may introduce noise and irrelevant data. Notably, our pipeline offers a dynamic solution that can quickly adapt to updates or modifications in the domain-specific document collection, eliminating the need for complete retraining. Additionally, it addresses the challenge of data scarcity by enabling the generation of instruction datasets from a limited set of initial documents, rendering it suitable for unpopular or specialized domains where comprehensive datasets are scarce. As a case study, we apply this approach to the domain of psychiatry, a field requiring specialized knowledge and sensitive handling of patient information. The resulting fine-tuned LLM demonstrates showcases the viability of the proposed approach and underscores its potential for widespread adoption across various industries and domains where tailored, accurate, and contextually relevant language models are indispensable.