PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches (2410.10870v3)

Abstract: As LLMs increasingly shape the AI landscape, fine-tuning pretrained models has become more popular than in the pre-LLM era for achieving optimal performance in domain-specific tasks. However, pretrained LLMs such as ChatGPT are periodically evolved, i.e., model parameters are frequently updated), making it challenging for downstream users with limited resources to keep up with fine-tuning the newest LLMs for their domain application. Even though fine-tuning costs have nowadays been reduced thanks to the innovations of parameter-efficient fine-tuning such as LoRA, not all downstream users have adequate computing for frequent personalization. Moreover, access to fine-tuning datasets, particularly in sensitive domains such as healthcare, could be time-restrictive, making it crucial to retain the knowledge encoded in earlier fine-tuned rounds for future adaptation. In this paper, we present PortLLM, a training-free framework that (i) creates an initial lightweight model update patch to capture domain-specific knowledge, and (ii) allows a subsequent seamless plugging for the continual personalization of evolved LLM at minimal cost. Our extensive experiments cover seven representative datasets, from easier question-answering tasks {BoolQ, SST2} to harder reasoning tasks {WinoGrande, GSM8K}, and models including {Mistral-7B, Llama2, Llama3.1, and Gemma2}, validating the portability of our designed model patches and showcasing the effectiveness of our proposed framework. For instance, PortLLM achieves comparable performance to LoRA fine-tuning with reductions of up to 12.2x in GPU memory usage. Finally, we provide theoretical justifications to understand the portability of our model update patches, which offers new insights into the theoretical dimension of LLMs' personalization.

• The paper introduces PortLLM, a framework for personalizing evolving Large Language Models using lightweight, training-free, and portable model patches.
• PortLLM achieves performance comparable to fine-tuning methods like LoRA while significantly reducing GPU memory usage by up to 12.2 imes on diverse tasks and LLMs.
• The research provides theoretical insights supporting the portability of patches across updated models, enabling more efficient and sustainable LLM adaptation.

PortLLM: Personalizing Evolving LLMs with Model Patches

The paper "PortLLM: Personalizing Evolving LLMs with Training-Free and Portable Model Patches" presents an innovative approach to personalize LLMs as they evolve over time without the computationally expensive process of continual fine-tuning. As the capabilities and availability of LLMs like Mistral and Llama continue to grow, the ability to efficiently adapt these models to domain-specific tasks is increasingly critical. The authors propose PortLLM, a framework designed to address the challenges faced by resource-constrained users in keeping up with the frequent updates of LLMs.

Main Contributions

PortLLM is characterized by two key innovations:

1. Initial Lightweight Model Update Patch: This patch is designed to capture domain-specific knowledge without requiring extensive computational resources. The patch is essentially a LoRA-derived adaptation matrix that encapsulates the specialized knowledge from the fine-tuned model version.
2. Seamless Plugging Across Iterations: PortLLM enables the effortless transfer of this domain knowledge across different iterations of an evolving LLM. By applying the initial domain-specific patch to newer versions of a pretrained model, users can maintain, and sometimes even enhance, task-specific performance without additional training.

Implementation and Results

Through exhaustive experiments over datasets from diverse domains—such as BoolQ, SST2, and GSM8K—the authors validate the effectiveness of PortLLM. Their method is evaluated against state-of-the-art LLM architectures, including Mistral-7B, Llama2, and Gemma2, affirming its generalizability and robustness.

• PortLLM achieves comparable performance to more traditional fine-tuning methods such as LoRA while reducing GPU memory usage by up to 12.2×, demonstrating significant efficiency improvements.
• In particular, improvements are noted across multiple downstream tasks, with zero-shot performance on PortLLM-enhanced models sometimes surpassing those of fully fine-tuned models.

The experimental results demonstrate that the proposed model patches consistently deliver significant performance enhancements when deployed on updated models of LLMs, thereby validating the utility and portability of the framework across various model architectures and continued pretraining datasets.

Theoretical Insights

Beyond empirical success, the paper advances theoretical justifications for the observed portability of the model patches. The analysis reveals that the difference between old and new personalization updates—the residual matrix—is negligible, thus validating the approximation wherein an older model patch can effectively substitute for newer, fine-tuned parameters. This insight contributes to a deeper understanding of the adaptability of personalization strategies within evolving NLP architectures.

Implications and Future Scope

PortLLM represents a strategic shift towards more sustainable, efficient AI systems that do not compromise on performance while adapting to frequent model updates. It challenges the traditional paradigm of continual comprehensive fine-tuning, suggesting that strategic parameter updates via patches can suffice for maintaining state-of-the-art performance on specialized tasks.

Looking ahead, the methods proposed in PortLLM have broad implications for the development of scalable AI applications, particularly in privacy-sensitive fields like healthcare, where access to fine-tuning datasets is restricted. An exciting avenue for future research could be exploring the use of portable model patches across completely different model architectures, providing even broader utility in the field of LLM development and application.

By addressing the computational and logistical burdens associated with frequent fine-tuning, PortLLM opens new possibilities in efficient AI deployment, fostering further innovation in the personalization of vast neural architectures. The approach not only promotes a sustainable pathway for future LLM developments but also invites further exploration of alternative training-free adaptations in the AI landscape.