LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning

TL;DR

LoRA-LiteE is a resource-efficient framework combining supervised fine-tuning, low-rank adaptation, and ensemble learning to improve chatbot preference tuning, achieving comparable performance to GPT-4 with less computational cost.

Contribution

This paper introduces LoRA-LiteE, a novel, scalable framework that effectively balances performance and computational efficiency for chatbot preference tuning.

Findings

LoRA-LiteE outperforms single large models under limited resources.

Achieves comparable results to GPT-4 trained with RLHF.

Enhances scalability and accessibility of preference-tuned chatbots.

Abstract

Effective preference tuning is pivotal in aligning chatbot responses with human expectations, enhancing user satisfaction and engagement. Traditional approaches, notably Reinforcement Learning from Human Feedback (RLHF) as employed in advanced models like GPT-4, have demonstrated considerable success in this domain. However, RLHF methods are often computationally intensive and resource-demanding, limiting their scalability and accessibility for broader applications. To address these challenges, this study introduces LoRA-Lite Ensemble (LoRA-LiteE), an innovative framework that combines Supervised Fine-tuning (SFT) with Low-Rank Adaptation (LoRA) and Ensemble Learning techniques to effectively aggregate predictions of lightweight models, which aim to achieve a balance between the performance and computational cost. Utilizing the Chatbot Arena benchmark dataset, we conduct a comprehensive comparative analysis among our LoRA-LiteE model, corresponding base models at different scales, and GPT-4 trained with RLHF. Our empirical results demonstrate that the proposed LoRA-LiteE model achieves comparable performance to un-finetuned GPT-4 and outperforms the single larger-scale models under limited resource constraints. These findings highlight that our LoRA-LiteE provides a feasible and efficient methodology for human preference prediction in chatbot systems, enhancing scalability and accessibility, and thereby broadening the applicability of preference-tuned chatbots in resource-constrained environments.