How Language Models Learn Taste: Brand Preference in LLMs
This paper asks where models learn that taste.
ProofPaper ↗
Key points
- Asked to name a running shoe brand, language models say Nike.
- Supervised fine-tuning teaches the model to answer and mostly leaves the pick unchanged.
- Preference optimization (DPO) teaches the model which answer is better, and the pick moves in all three pipelines, by 11 to 24 points.
- For brands, a recommendation depends less on what a model knows about them than on whether they fit this taste.
Sources (1)
- [1]How Language Models Learn Taste: Brand Preference in LLMsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 02:26 PM
This paper asks where models learn that taste.
Asked to name a running shoe brand, language models say Nike.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 20, 2026Answering in the User's Variety: Calibrated Accommodation Pairs for Post-Training
- Oct 19, 2026Amazon SageMaker JumpStart: 2026 year-to-date in review
- Oct 19, 2026Introducing Clairic, a new System One class of extraction models
- Oct 19, 2026TokenGate: Adapting LLMs for Fast and Memory-Efficient Prefill with Learned Token Pruning
- Oct 18, 2026CHSM8: A Chess Transformer that Follows Natural-Language Style Prompts
- Oct 18, 2026Aligning Language Models with Indigenous Community Cosmovision through Constitutional AI