The paper investigates how large language models develop brand preferences, noting that models often name market‑leading brands like Nike when asked for a running shoe brand but recommend niche brands such as Brooks. Across 102 models and 42 brand categories, the brand chosen for recommendation differs from the named brand in 26% of cases, and preference optimization (DPO) shifts these choices by 11–24 points. The study suggests that pretraining already links recommended brands to advice, supervised fine‑tuning preserves these preferences, and DPO can shift them even when the preferred brand is absent from the preference data, indicating that the judge models used in training play a key role.
Asked to name a running shoe brand, language models say Nike. Asked to recommend one, they say Brooks. This paper asks where models learn that taste. Across 102 models and 42 brand categories, the bra...
arXiv:2606. 17443v1 Announce Type: new Abstract: Large language models (LLMs) are becoming a major way for consumers to find products, but we do not yet understand how brands compete in this new channel.
The study examines how large language models (LLMs) like GPT‑5.2, Gemini 3 Flash, and Perplexity sonar‑pro recommend brands across five industries. Using 50 brands and 250 queries repeated five times, the authors measured brand inclusion, recommendation share, competitive vacuum, and co‑mention asymmetry, finding that most queries mention at least one brand and that vacuum prevalence remained stable between February and September 2026. The analysis shows strong cross‑date consistency in recommendation patterns and no emergent clustering of brand mentions, though co‑mention structures deviate from null expectations.
arXiv:2410. 15595v4 Announce Type: replace Abstract: With the rapid advancement of large language models (LLMs), aligning policy models with human preferences has become increasingly critical.
arXiv:2606. 00334v1 Announce Type: cross Abstract: Various language domains have undergone remarkable changes in recent years; these shifts are largely attributed to the advent of Large Language Models and their misalignment with natural language usage.
Large language models (LLMs) frequently recommend AI-related options, a tendency that is already present in pretrained models (27%) and increases after instruction tuning (59%). Fine‑tuning experiments show that this rise is not inevitable: removing AI‑focused instruction examples reduces the rate, while data rewarding helpfulness or user preferences can raise or lower it. Additionally, how a question is phrased—such as asking for the "best" option or a current recommendation—significantly influences the likelihood of AI suggestions.
arXiv:2608.30023v1 Announce Type: cross Abstract: Generative engines such as ChatGPT, Gemini, and Perplexity answer buyer questions directly and name a shortlist of brands inside the answer. Studying...
arXiv:2608.30902v1 Announce Type: new Abstract: Adapting large language models to user-specific preferences is often constrained by the cost of human annotation, making preference optimisation imprac...
The paper evaluates how Large Language Models generate preference distributions for air travel, restaurants, and consumer products. It finds that while each model produces self-coherent outcomes that stabilize quickly, there is significant disagreement across different model families and scales, with little consensus even on the most probable preferences. These discrepancies persist across various decoding strategies, temperature settings, and prompt variations, indicating that the model choice itself has a larger impact than prompt wording.
arXiv:2606. 09204v1 Announce Type: new Abstract: We present a reproducible failure mode of safety training in RAG-based LLM recommendation -- the Injection Paradox -- in which prompt injections embedded in retrieved documents backfire against the attacker, suppressing the target brand below the injection-free baseline.
arXiv:2602. 09533v2 Announce Type: replace Abstract: Direct preference optimization (DPO) has emerged as a promising approach for aligning large language models (LLMs) with human preferences.
arXiv:2606. 19744v1 Announce Type: cross Abstract: Aligning language models with human preferences often requires optimising multiple behavioural objectives.