BUILDING …

[Ensuring Data Integrity in LLM Training]

Proof of

Training

A Showcase Site by Dirk Lach

Using Merkle tree proofs to verify data integrity and storing these proofs on blockchain.

By POT Team

Proof of Training (PoT) ensures data integrity in large language models (LLMs) by storing Merkle roots, derived from the training data, on the blockchain. This guarantees the authenticity and immutability of the training data, preventing tampering and ensuring the reliability and trustworthiness of LLMs.

[ READY TO STAND OUT? ]

01

Data Integrity

[ LLM Training ]

Data integrity is vital for training LLMs, as compromised data can lead to inaccurate models and unreliable outputs. Ensuring data quality and preventing tampering remain key challenges in machine learning.

02

data verification

[ Merkle tree & BLOCKCHAIN ]

Merkle tree is widely used in blockchain systems to verify large datasets efficiently. PoT applies this technology to LLM training data integrity, offering promising solutions for data verification and immutability.

[ Related work ]

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explore the proof of training (Pot)

PoT

METHODOLOGY

01

Merkle Tree

A Merkle tree is constructed from the split training data blocks, forming a single root hash (Merkle root).

02

Verification

Verification involves recomputing hashes, comparing to the Merkle root. Merkle tree detects data modifications efficiently.

03

Interaction

Merkle root uploaded to blockchain via smart contracts, ensuring immutability and public verifiability.

04

Decentralized

Blockchain offers decentralized, tamper-proof ledger for recording training data state at different checkpoints.

AND MANY
MORE

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[ white paper ]

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Implementation Steps

01.

Data Preparation - Clean, Normalize, Divide

02.

Merkle Tree Generation

03.

Blockchain Interaction - Smart Contract

04.

Training Integration - Pipeline

05.

Verification Tool

06.

Evaluation - Computational Burdens

[ PoT provides a robust solution to the problem of data integrity in AI training. ]