REFINE-AF: A Task-Agnostic Framework to Align Language Models via Self-Generated Instructions using Reinforcement Learning from Automated Feedback (2505.06548v1)

Abstract: Instruction-based LLMs have proven effective in numerous few-shot or zero-shot 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.

Analysis of the REFINE-AF Framework for Instruction-Aligned LLMing

The paper "REFINE-AF: A Task-Agnostic Framework to Align LLMs via Self-Generated Instructions using Reinforcement Learning from Automated Feedback" explores an innovative approach to enhancing the capabilities of LLMs in generating, understanding, and executing natural language instructions. The main focus is on mitigating the challenges associated with the sourcing of human-annotated instruction datasets, which are often scarce and costly.

Methodological Innovations

The proposed REFINE-AF framework is noteworthy for its use of smaller, open-source LLMs like LLaMA 2-7B, LLaMA 2-13B, and Mistral 7B. By leveraging these models, the research circumvents the limitations of existing methods that rely on large proprietary models such as GPT-3.5. The REFINE-AF framework consists of three main stages:

1. Instruction Generation: This stage employs a semi-automated method to generate diverse task instructions, bootstrapping from a small set of manually curated seed instructions. The framework emphasizes maximizing diversity in language and instruction type.
2. Reinforcement Learning from Automated Feedback (RLAF): The second stage introduces a mechanism to refine input-output pairs generated from instructions by employing reinforcement learning. Here, automated feedback replaces human feedback, using reward criteria like naturalness, coherence, and preference scores quantified through models like oasst-rm-pythia-1.4b.
3. Instance Generation: The final stage involves synthesizing high-quality triplet datasets (instruction, input, output) for downstream finetuning tasks.

Empirical Findings and Contributions

The empirical results indicate substantial improvements over previous frameworks, showcasing performance enhancements in up to 66% of tested tasks when evaluated against the Super-NI benchmark, reinforcing the viability of smaller-scale models in instruction generation tasks. The REFINE-AF method also significantly outperformed the baseline self-instruction method across diverse task categories under zero-shot settings, showcasing its robust generalization capabilities.

Key numerical results include achieving an average ROUGE-L improvement of 64.39% in the task datasets generated using LLaMA 2-7B and 66.39% using LLaMA 2-13B. This evidences the efficacy of REFINE-AF in generating quality instruction sets that enhance model performance on standard benchmarks.

Furthermore, the research contributes a synthetic dataset comprising 45,000 generated instructions tailored for various NLP contexts. This dataset sets a precedent for future open-source research efforts in the domain of LLM instruction tuning.

Implications and Future Directions

The REFINE-AF framework demonstrates significant potential to optimize the instruction-following abilities of LLMs without dependence on extensive human annotation. This approach not only reduces the operational costs associated with large-scale model finetuning but also broadens access through the use of open-source models.

Theoretically, the results suggest potential avenues for improving model interpretability and response coherence through automated feedback mechanisms. Practically, the reduction in reliance on large, expensive models democratizes the implementation of advanced NLP solutions.

Looking forward, the paper opens several future research avenues. Exploring the extension of this framework to multimodal applications, fine-tuning reward mechanisms for higher semantic alignment, and integrating more sophisticated diversity measures during instruction generation could further enhance model robustness. Additionally, experimental validation encompassing broader NLP tasks beyond the conventional benchmarks will help cement the utility of REFINE-AF in real-world applications.

In conclusion, REFINE-AF represents a significant step towards more accessible and cost-effective instruction alignment in LLMs while maintaining competitive performance through strategic integration of semi-automated techniques and reinforcement learning.