Progress on provable inference for large language models - Anthropic
By Google News AI ·
Roy Rinberg (Anthropic), Alex Allain (Anthropic); Alessandro Chiesa (EPFL, StarkWare), Miranda Christ (MIT, RESI), Rahul Dalal (Anthropic STEM Fellows Program, Succinct), Giacomo Fenzi (EPFL), Ben Mayer (Anthropic Fellows Program, MIT), Guy Weissenberg (EPFL)
When you talk to a friend in person, you have many ways to know it is really them: you can see them, hear them, shake their hand. When you talk to an AI model, you have almost none. A user querying a model today cannot check that the responses came from the specific model that a developer claims is responding, as opposed to one secretly substituted in by a routing service for cost reasons, or even by an attacker who has tampered with the token stream. Judging from the model’s output alone is unreliable, even if it produces high-quality responses. Cryptographic proofs of inference solve this problem: they let a model provider commit to a model and then prove that each response was produced by that model. Such proofs could enable applications ranging from consumer products to internal security, and could contribute to a toolkit for verifying compliance with AI pacing requirements.
Specifically, we have made progress on a powerful type of cryptographic proof known as a “zkSNARK”, which is uniquely fit for provable inference (owing to its strong hiding and efficiency properties). In this post, we share some of that progress, primarily through a system we call FlashProve. A brief technical summary is as follows:
We release our code, Lean proofs, and documentation under the Apache 2.0 license.1 This research post is meant to share intermediate progress, and not a completed research agenda. We are sharing this explicitly with the goal that others look at what we have done, take it, and improve it.
When a user sends a…
Originally published by Google News AI