English(EN) Understanding ChatGPT Citations: Evidence from Production Web Search Across Model Generations

研究分析了跨模型版本的ChatGPT引用选择

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-11 06:20

一篇新的研究论文探讨了像ChatGPT这样的生成式AI模型如何选择引用的网页。该研究分析了四种不同的ChatGPT网络设置，观察了模型如何重写查询、检索潜在来源并最终选择引用。研究发现，频繁检索到的候选内容更有可能被引用，并且像Google Search和OpenAI Responses API这样的外部工具可以部分预测这些引用，尽管它们仅占引用URL的约四分之一。 AI

影响为生成引擎优化（GEO）以及影响AI响应中内容可见性的因素提供了见解。

排序理由研究论文分析AI模型行为。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 研究论文分析AI模型行为。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 5 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xiaokui Xiao · 2026-10-11 06:20

理解ChatGPT引用：跨模型代的生产网络搜索证据

Generative engines such as ChatGPT Web retrieve webpages to answer user queries, but only a subset are shown as citations. Understanding this retrieval-to-citation process is important for generative engine optimization (GEO), which aims to improve content visibility in generativ…

本文如何被排名

报道来源 [1]

理解ChatGPT引用：跨模型代的生产网络搜索证据

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