arXiv cs.CL· Veronika Solopova, Ivan Havlytskyi, Viktoria Skorik, Ostap Vykhopen·· 6 天前AI 评分24
GeopoliticalQA：面向情报报告的检索与生成基准
GeopoliticalQA: Benchmarking Retrieval and Generation over Intelligence Reports
AI 导读
研究者发布 GeopoliticalQA 基准，覆盖 415 份开源地缘政治与战略情报报告、5,525 条文档锚定问答对，涵盖事实、推断、比较和风险四类问题。评测 15 个检索系统和 6 个开放权重 LLM 后发现，文档级检索表现相近，但在更难的 chunk 级检索上，中等规模开源编码器 bge-m3 可匹敌大其一个数量级的模型并优于商业 embedding API。
正文 · 原文
Abstract:Geopolitical and strategic intelligence reports constitute a rich source of expert knowledge and have increasing value in the current political climate, but their heterogeneity and analytical complexity pose challenges for retrieval-augmented question answering. We introduce a benchmark for grounded question answering over 415 open-source geopolitical and strategic intelligence reports, comprising 5,525 document-grounded question-answer pairs spanning factual, inferential, comparative, and risk-oriented questions. We propose a document-aware generation pipeline that profiles reports before generating questions and validate dataset quality through expert annotation and an LLM-as-judge protocol. Using the benchmark, we evaluate 15 retrieval systems and six open-weight LLMs for RAG. We find that models perform similarly on document-level retrieval, while at the harder chunk level retrieval quality is not driven by embedding-model scale: bge-m3, a mid-size open encoder matches an order of magnitude larger ones and outperforms commercial embedding APIs. In contrast, answer generation remains challenging even given the gold evidence, particularly for inferential and risk-oriented questions.
| Subjects: | Computation and Language (cs.CL); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2610.12617 [cs.CL] |
| (or arXiv:2610.12617v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12617 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Veronika Solopova [view email]
[v1]
Thu, 8 Oct 2026 18:35:13 UTC (67 KB)
来源：arXiv cs.CL · arxiv.org