Summary of Svip: Towards Verifiable Inference Of Open-source Large Language Models, by Yifan Sun et al.
SVIP: Towards Verifiable Inference of Open-source Large Language Models
by Yifan Sun, Yuhang Li, Yue Zhang, Yuchen Jin, Huan Zhang
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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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 The paper formalizes the problem of verifiable inference for Large Language Models (LLMs) and proposes a secret-based protocol, called SVIP, to ensure honest computation. SVIP leverages intermediate outputs from LLMs as unique model identifiers, allowing users to verify whether computing providers are acting honestly. The protocol integrates a secret mechanism to enhance security. Extensive experiments demonstrate SVIP’s accuracy, generalizability, computational efficiency, and resistance to various attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem with Large Language Models that are too big to use on personal devices. When you want to use one of these models, you need to send your data to a computing service provider who can run the model for you. But what if this provider is secretly using a smaller, less powerful model instead? This could result in bad outputs and profit for the provider. The paper proposes a way to verify that the provider is using the correct model by looking at intermediate steps during the calculation. It also adds extra security features to make sure everything works correctly. |
Keywords
* Artificial intelligence * Inference
