Bye-bye Bias: What to Consider When Training Generative AI Models on Subjective Marketing Metrics
Journal
NIM Marketing Intelligence Review
Type
journal article
Date Issued
2024-04-25
Author(s)
Abstract
The advent of large language models (LLMs) and multi-modal generative artificial intelligence (AI) systems such as ChatGPT, Dall-E and Midjourney has ushered in a new era of innovation and exploration, promising a wide array of use cases for marketing, ranging from content creation, personalized recommendations and insight generation, to internal process optimization. Off-the-shelf LLMs often exhibit remarkable “zero-shot” capabilities for these use cases, allowing them to generate content or make predictions without explicit training on specific tasks or the specific brand context. However, these models are trained on vast data sets scraped from the Internet, such as Common Crawl or LAION, that contain little information on the types of perceptual measures marketing is often interested in, such as perceived brand image and consumer engagement, or that lack the specific brand context. For example, some services offer direct ad creation hand in hand with search optimization. These ads are designed and optimized for high average click-through rates but do not provide brand differentiation and, at worst, can undermine brand image. Unlocking the true power of GenAI requires fine-tuning models to the specific brand context, which is often reflected
Language
English
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Walter de Gruyter GmbH
Volume
16
Number
1
Subject(s)
Division(s)