GeopoliticalQA: Benchmarking Retrieval and Generation over Intelligence Reports
Quick Answer
The study introduces a benchmark for grounded question answering using 415 geopolitical intelligence reports, evaluating 15 retrieval systems and six open-weight LLMs.
Quick Take
Notably, the mid-size bge-m3 encoder outperformed larger models and commercial APIs in document-level retrieval, while answer generation remains challenging, especially for inferential questions.
Key Points
- Benchmark includes 5,525 question-answer pairs from geopolitical reports.
- bge-m3 encoder matches larger models in document-level retrieval.
- Answer generation is particularly difficult for inferential and risk-oriented questions.
- Expert annotation and -as-judge protocols validate dataset quality.
- Study highlights challenges in retrieval-augmented question answering.
DeepSignal Analysis
What happened
A benchmark for grounded question answering was introduced, utilizing 415 geopolitical intelligence reports and evaluating 15 retrieval systems alongside six open-weight LLMs. The mid-size bge-m3 encoder outperformed larger models in document-level retrieval, while answer generation, particularly for inferential questions, proved challenging.
Key evidence
- The benchmark consists of 5,525 document-grounded question-answer pairs derived from 415 geopolitical intelligence reports.
- The bge-m3 encoder, a mid-size model, matched the performance of significantly larger models and outperformed commercial embedding APIs in document-level retrieval.
- Answer generation remains difficult, especially for inferential and risk-oriented questions, even when gold evidence is available.
Why it matters
This research highlights the complexities involved in retrieval-augmented question answering, particularly in the context of geopolitical intelligence. The findings suggest that model size does not always correlate with retrieval performance, which could influence future model development and application in intelligence analysis. The challenges in answer generation indicate a need for further advancements in LLM capabilities.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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)
— Originally published at arxiv.org
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