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[2410.10481] Model-based Large Language Model Customization as Service
Summary
The paper presents Llamdex, a framework for customizing large language models (LLMs) as a service, allowing clients to upload domain-specific models while preserving data privacy and enhancing accuracy.
Why It Matters
As LLMs become integral in various applications, ensuring their effectiveness in domain-specific tasks while maintaining user privacy is crucial. Llamdex addresses these challenges by enabling customization without exposing sensitive data, thus promoting safer AI practices.
Key Takeaways
- Llamdex allows clients to upload domain-specific models instead of data for LLM customization.
- The framework incorporates differential privacy with reduced noise, enhancing model effectiveness.
- Llamdex improves domain-specific accuracy by up to 26% compared to traditional methods.
- It maintains inference efficiency comparable to original LLM services.
- The approach addresses privacy concerns associated with data uploads in LLM customization.
Computer Science > Machine Learning arXiv:2410.10481 (cs) [Submitted on 14 Oct 2024 (v1), last revised 15 Feb 2026 (this version, v5)] Title:Model-based Large Language Model Customization as Service Authors:Zhaomin Wu, Jizhou Guo, Junyi Hou, Bingsheng He, Lixin Fan, Qiang Yang View a PDF of the paper titled Model-based Large Language Model Customization as Service, by Zhaomin Wu and 5 other authors View PDF HTML (experimental) Abstract:Prominent Large Language Model (LLM) services from providers like OpenAI and Google excel at general tasks but often underperform on domain-specific applications. Current customization services for these LLMs typically require users to upload data for fine-tuning, posing significant privacy risks. While differentially private (DP) data synthesis presents a potential alternative, its application commonly results in low effectiveness due to the introduction of excessive noise on data for DP. To overcome this, we introduce Llamdex, a novel framework that facilitates LLM customization as a service, where the client uploads pre-trained domain-specific models rather than data. This client-uploaded model, optionally protected by DP with much lower noise, is inserted into the base LLM via connection modules. Significantly, these connecting modules are trained without requiring sensitive domain data, enabling clients to customize LLM services while preserving data privacy. Experiments demonstrate that Llamdex improves domain-specific accuracy by up t...
