Triple-Entry Accounting as a Means of Auditing Large Language Models

Abstract

The usage of Large Language Models (LMMs) and their exponential progress has created a Cambrian Explosion in the development of new tools for almost every field of science and technology, but also presented significant concerns regarding the AI ethics and creation of sophisticated malware and phishing attacks. Moreover, several worries have arisen in the field of dataset collection and intellectual property in that many datasets may exist without the license of the respective owners. Triple-Entry Accounting (TEA) has been proposed by Ian Grigg to increase transparency, accountability, and security in financial transactions. This method expands upon the traditional double-entry accounting system, which records transactions as debits and credits in two separate ledgers, by incorporating a third ledger as an independent verifier via a digitally signed receipt. The utilization of a digital signature provides evidentiary power to the receipt, thus reducing the accounting problem to one of the presence or absence of the receipt. The integrity issues associated with double-entry accounting can be addressed by allowing the parties involved in the transaction to share the records with an external auditor. This manuscript proposes a novel methodology to apply triple-entry accounting records on a publicly accessed distributed ledger technology medium to control the queries of LLMs in order to discourage malicious acts and ensure intellectual property rights.