VQV Signal
Sampled Layerwise Proofs (SLP) is a protocol that verifies outsourced language model inference by committing boundary activations and selectively proving subsets of inference chunks. This approach supports verifiable computation across models from GPT-2 up to 70 billion parameters.
Sampled Layerwise Proofs (SLP) is a protocol that verifies outsourced language model inference by committing boundary activations and selectively proving subsets of inference chunks. This approach supports verifiable computation across models from GPT-2 up to 70 billion parameters.
AI-assisted summary based on the listed source.
Verifying outsourced language-model inference requires a precisely identified computation and an audit whose cost a service can afford. We present Sampled Layerwise Proofs (SLP), a protocol and prototype that commits the boundary activations of every chunk of an inference trace, absorbs all commitments before any...
SLP provides a cost-effective method for verifying large-scale LLM inference, ensuring trust in outsourced computations without prohibitive audit costs. This enhances reliability and accountability in deploying large language models in external services.
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GPT-2 has a source-backed pricing with coverage spanning pricing.
VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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