REFINE-AF: A Task-Agnostic Framework to Align Language Models via Self-Generated Instructions using Reinforcement Learning from Automated Feedback

TL;DR

This paper introduces REFINE-AF, a task-agnostic framework that uses self-generated instructions and reinforcement learning to improve open-source LLMs, reducing costs and human effort while enhancing task performance.

Contribution

The paper presents a semi-automated, RL-enhanced instruction generation framework for open-source LLMs, outperforming prior methods that relied on expensive API-only models.

Findings

RL-based frameworks improve performance in 63-66% of tasks

Open-source LLMs can effectively generate instructions with reduced human effort

Cost-effective alternative to API-dependent instruction tuning

Abstract

Instruction-based Large Language Models (LLMs) have proven effective in numerous few-shot or zero-shot Natural Language Processing (NLP) tasks. However, creating human-annotated instruction data is time-consuming, expensive, and often limited in quantity and task diversity. Previous research endeavors have attempted to address this challenge by proposing frameworks capable of generating instructions in a semi-automated and task-agnostic manner directly from the model itself. Many of these efforts have relied on large API-only parameter-based models such as GPT-3.5 (175B), which are expensive, and subject to limits on a number of queries. This paper explores the performance of three open-source small LLMs such as LLaMA 2-7B, LLama 2-13B, and Mistral 7B, using a semi-automated framework, thereby reducing human intervention, effort, and cost required to generate an instruction dataset for fine-tuning LLMs. Furthermore, we demonstrate that incorporating a Reinforcement Learning (RL) based training algorithm into this LLMs-based framework leads to further enhancements. Our evaluation of the dataset reveals that these RL-based frameworks achieve a substantial improvements in 63-66% of the tasks compared to previous approaches.