Summary of Vtune: Verifiable Fine-tuning For Llms Through Backdooring, by Eva Zhang et al.
vTune: Verifiable Fine-Tuning for LLMs Through Backdooring
by Eva Zhang, Arka Pal, Akilesh Potti, Micah Goldblum
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 paper proposes a solution to verify that large language models (LLMs) are fine-tuned correctly by third-party services. The proposed method, called vTune, uses a small number of backdoor data points to provide a statistical test for verification. Unlike existing works, vTune can scale to verification of fine-tuning on state-of-the-art LLMs and can be used with both open-source and closed-source models. The authors test their approach across several model families and sizes as well as across multiple instruction-tuning datasets, finding that the statistical test is satisfied with p-values on the order of ∼10^(-40), with no negative impact on downstream task performance. Furthermore, they demonstrate the method’s robustness to various attacks that attempt to subvert vTune. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper finds a way to make sure that when someone fine-tunes a language model, it’s actually customized for them and not just the same old model again. The researchers create a special test that uses a few extra pieces of information to check if the model was really fine-tuned or if it’s just the same as always. This method is important because right now, people don’t know what’s going on when they fine-tune language models and can’t trust that it’s being done correctly. This new approach makes sure that language models are customized for each person, without affecting their performance. |
Keywords
» Artificial intelligence » Fine tuning » Instruction tuning » Language model
