LLM Advertisement based on Neuron Auctions

TL;DR

This paper introduces Neuron Auctions, a novel mechanism for embedding advertisements in LLMs by auctioning internal neurons, balancing revenue, user experience, and semantic coherence.

Contribution

It proposes a parametric auction framework based on interpretability of LLM neurons, enabling independent control and strategy-proof advertising integration.

Findings

Neuron Auctions maintain discourse quality while optimizing revenue.

Brand-specific neurons activate in orthogonal subspaces, enabling auction design.

The framework dynamically prices interventions to prevent overly aggressive advertising.

Abstract

As Large Language Models (LLMs) transition into conversational agents, generative advertising emerges as a crucial monetization strategy. However, embedding advertisements within unstructured LLM outputs introduces a critical trilemma: balancing advertiser payoffs, platform revenue, and user experience. Existing methods, such as prompt injection or rigid position slots, disrupt semantic coherence and lack a parametric framework for independent control, rendering rigorous mechanism design intractable. To bridge this gap, we introduce Neuron Auctions, a novel paradigm that shifts the auction object from the surface text space to the LLM's internal representations. Leveraging mechanistic interpretability, we identify brand-specific feed-forward network (FFN) neurons and demonstrate that competing brands activate within approximately orthogonal subspaces. This near-perfect independence allows us to define continuous, disentangled intervention budgets (specifically, neuron counts and amplification factors) as auctionable commodities. Building on this computational carrier, we design a continuous menu-based auction mechanism that naturally guarantees strategy-proofness and optimizes revenue for the platform. By explicitly incorporating a user utility penalty into the platform's optimization objective, our framework dynamically prices out overly aggressive interventions. Extensive experiments demonstrate that Neuron Auctions effectively preserve natural discourse quality while achieving an optimal alignment between commercial incentives and user satisfaction.