AI 中文总结
研究人工智能大语言模型引擎对全球冲突信息环境的塑造，通过向五个引擎询问28场冲突问题并分析答案及相关网站，发现可检索记录稀少会致引擎出错，存在生成引擎优化源头优化，探讨了研究意义、政策影响及未来机遇挑战。
AI 中文摘要
人工智能问答引擎在回答分析师、学者和公众关于和平与冲突问题时所占份额日益增加。大语言模型在某些情况下会产生幻觉，本文对此展开研究。首先向五个领先的问答引擎询问了一系列关于28场冲突的问题，并根据文献证据对其5460个答案进行评分。发现围绕特定冲突的可检索记录越稀少，引擎就越容易虚构、错误归因和错误计数。通过对1048个被人工智能大语言模型提取冲突事实的网站分析，发现生成引擎优化的源头优化已在发生。阐述了这些发现对学术研究的意义，以及对政策的影响，并讨论了未来研究机遇与挑战。
英文摘要
Artificial Intelligence (AI) answer engines now field a growing share of the questions that analysts, scholars, and the public ask about issues of peace and conflict. Large Language Models (LLMs) are known to hallucinate under certain conditions, but do these errors have discernible patterns when they are asked about conflicts, and if so what can that teach us about the changing global conflict information environment? To answer, we first asked a battery of questions about 28 conflicts to five leading answer engines and scored their 5,460 answers against documented evidence. We found that the thinner the retrievable record around a given conflict, the more the engines invent, misattribute, and miscount. Thin records don't just encourage hallucination, but create structural exposure to mis- and disinformation, because they are the easiest records to warp through Generative Engine Optimization (GEO) to bias engine responses. Through an analysis of 1,048 websites that the AI LLMs pulled conflict facts from, we found that GEO source optimization is already happening, and while state-partisan digital capture remains incipient it is rapidly growing. We explain what these findings mean for scholarship with the rise of GEO information warfare, and for policy argue for a return to the deep local monitoring and translation-based research that AI tools cannot replicate, closing with a discussion of future research opportunities and challenges in this fast-moving space.
