Summary of Pdss: a Privacy-preserving Framework For Step-by-step Distillation Of Large Language Models, by Tao Fan et al.
PDSS: A Privacy-Preserving Framework for Step-by-Step Distillation of Large Language Models
by Tao Fan, Yan Kang, Weijing Chen, Hanlin Gu, Yuanfeng Song, Lixin Fan, Kai Chen, Qiang Yang
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a framework called PDSS (Privacy-Preserving Domain-Specific Distillation) to address two major challenges in using large language models (LLMs) for domain-specific tasks: domain-specific knowledge privacy and constrained resources. The framework uses a server-client architecture, where the client transmits perturbed prompts to the server’s LLM for rationale generation. The generated rationales are then decoded by the client and used to enrich the training of task-specific small language models (SLMs) within a multi-task learning paradigm. The PDSS framework introduces two privacy protection strategies: the Exponential Mechanism Strategy and the Encoder-Decoder Strategy, balancing prompt privacy and rationale usability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PDSS helps solve problems with using large language models for specific tasks by keeping sensitive information private while still getting good results. It works by having a client send special prompts to an LLM on a server, which gives reasons for its answers. The client then uses these explanations to help train smaller language models that are better at specific jobs. |
Keywords
» Artificial intelligence » Distillation » Encoder decoder » Multi task » Prompt
