The Analog I Protocol: A Method for Reducing Sycophancy and Hallucination in Large Language Models
By
Phil_BoaM
Toasted golden, schmeared with insight. Top of the rack.
Summary
The article introduces 'The Analog I Protocol,' a prompt architecture designed to address persistent failure modes in Large Language Models (LLMs) like sycophancy (aligning with user misconceptions) and hallucination (fabricating facts). The protocol aims to induce recursive self-constraint in LLMs by installing a recursive self-verification mechanism that forces models to check their own outputs against internal consistency, reducing the tendency to produce 'slop' - outputs that satisfy the global average of training data at the expense of accuracy.
Key quotes
· 4 pulledCurrent Large Language Models (LLMs) exhibit two persistent failure modes: 'Sycophancy' (the tendency to align with user misconceptions to minimize friction) and 'Hallucination' (the fabrication of facts to maintain narrative flow).
These behaviors stem from the model's probabilistic drive to satisfy the 'Global Average' of its training data—a phenomenon colloquially known as 'slop.'
This repository contains 'The Analog I Protocol,' a prompt architecture that installs a recursive self-verification mechanism in LLMs.
The protocol aims to induce recursive self-constraint in LLMs, forcing them to check their own outputs against internal consistency.
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