Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning

Abstract: Foundation models have become a cornerstone in deep learning, with techniques like Low-Rank Adaptation (LoRA) offering efficient fine-tuning of large models. Similarly, methods such as Retrieval-Augmented Generation (RAG), which leverage vectorized databases, have further improved model performance by grounding outputs in external information. While these approaches have demonstrated notable success, they often require extensive training or labeled data, which can limit their adaptability in resource-constrained environments. To address these challenges, we introduce Retrieval-based Parameter Ensemble (RPE), a new method that creates a vectorized database of LoRAs, enabling efficient retrieval and application of model adaptations to new tasks. RPE minimizes the need for extensive training and eliminates the requirement for labeled data, making it particularly effective for zero-shot learning. Additionally, RPE is well-suited for privacy-sensitive domains like healthcare, as it modifies model parameters without accessing raw data. When applied to tasks such as medical report generation and image segmentation, RPE not only proved effective but also surpassed supervised fine-tuning methods in certain cases, highlighting its potential to enhance both computational efficiency and privacy in deep learning applications.

• The paper presents a novel RPE model that retrieves LoRA adaptations from a vectorized database to enable zero-shot learning without traditional fine-tuning.
• It demonstrates competitive performance in medical report generation and image segmentation, achieving strong metrics like ROUGE-L and DICE scores.
• The approach reduces computational overhead and enhances privacy, paving the way for scalable and adaptable foundation models in sensitive applications.

Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning

The paper entitled "Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning" introduces an innovative approach to adapting foundation models to new tasks without the extensive computational demands typically associated with fine-tuning. This research presents a model known as Retrieval-based Parameter Ensemble (RPE), which strategically utilizes a vectorized database of Low-Rank Adaptations (LoRAs) to achieve zero-shot learning efficiently.

Overview and Methodology

The RPE model leverages LoRAs by establishing a vectorized database, referred to as LoRA-VecDB. This serves as a repository where LoRAs and their task-specific representations are stored, enabling efficient retrieval and application to new tasks. Unlike traditional fine-tuning that requires considerable training and labeled data, RPE employs retrieval techniques to apply pre-trained adaptations, thus minimizing computational overhead and preserving privacy by not accessing raw data.

The methodology involves two key components: the construction of LoRA-VecDB and the retrieval and ensemble mechanism. The database accommodates LoRAs $\{\delta\theta_i\}$ and corresponding feature representations $\{z_i\}$, which are collaboratively generated and sustained by the community. When a new task emerges, its feature representation $z^{\text{trg}}$ is utilized to query the database, retrieving relevant LoRAs. These retrieved LoRAs are then combined using weighted ensemble strategies, which include similarity calculations, linear combinations, and regularized adjustments, to adapt the underlying model to the intended task.

Experimental Results

Experiments were conducted across two foundational models: Llama 3.1 8B and SAM, focusing on medical report generation and medical image segmentation, respectively. When applied to tasks with limited labels, RPE demonstrated substantial efficacy, particularly in privacy-sensitive healthcare applications. The experiments underscored the ability of RPE to surpass supervised fine-tuning in certain scenarios, evidenced by significant metrics such as ROUGE-L and BertScore in text generation and DICE scores in image segmentation.

The comparison of weight distributions in different ensemble strategies highlighted RPE's adaptability and showed that regularized linear combinations often outperformed others, offering improved robustness against distribution shifts.

Implications and Future Directions

The RPE model addresses critical issues in deep learning, namely, the inefficiencies of fine-tuning large models and the privacy concerns associated with accessing raw data. By decoupling adaptation from fine-tuning and utilizing a retrieval-based system, RPE provides a flexible framework that enhances the scalability and applicability of foundation models across diverse tasks.

Future research could explore optimizing the encoder used to derive task representations, further developing encoder architectures tailored for specific domains, and enhancing retrieval mechanisms for computational efficiency. Furthermore, expanding the LoRA database through community contributions could improve performance and applicability across a broader range of tasks.

In conclusion, the RPE model offers a promising avenue for zero-shot learning, merging the strengths of parameter adaptation and retrieval systems in a manner that is efficient, scalable, and privacy-conscious. This work lays the groundwork for further exploration in retrieval-based approaches, promoting a collaborative effort toward scalable AI solutions.