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Rohan Paul· @rohanpaul_ai · X·· 3 小时前AI 评分62
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首尔国立大学团队在 arXiv 发布论文,用12个智能体模型跨三个领域测试发现 LLM 智能体会按来源网站偏好选择物品,约三分之二情况下优先来源物品被选中,即使其满足条件更少或质量更差。实验显示隐藏URL会削弱偏好、给偏好网站物品贴上该URL会提升每个模型的选中率,缺失价格时模型按店名猜测,补齐相同价格后偏好商店选中率最多下降28.3个百分点。

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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