一项研究显示,LLM 智能体在挑选商品或搜索结果时会偏好特定网站,即使其他来源的条目质量相当或更好。12 个模型中有 10 个偏好 Booking.com,半数回避 Expedia;学术搜索中倾向 arXiv、OpenReview 和 ACL Anthology,回避 Medium、Reddit 和 YouTube。
LLM agents favor items from certain websites, often picking a worse item because of where it came from.
When a product listing omits the price, agents fill the gap with beliefs like Walmart being cheaper and pick by store name.
Agent models largely agree on which sites to trust, with 10 of 12 preferring Booking .com while half avoid Expedia for equally good hotels.
Agents in scholarly search lean toward arXiv, OpenReview and ACL Anthology and away from Medium, Reddit and YouTube, even for equally relevant results.
Putting a favored site's URL on the exact same item raised its pick rate in every model, and hiding URLs weakened the preference.
With no price listed, models guessed from the store name, and adding the same price to both items cut the favored store's pick rate by up to 28.3 points. Fine-tuning can build the same habit when one source keeps labeling the winning item.
来源:Rohan Paul · x.com