Auto71%Harbor: pendingKnowledge & Reasoning

GeopoliticalQA

GeopoliticalQA: Benchmarking Retrieval and Generation over Intelligence Reports

A benchmark for grounded QA over 415 geopolitical intelligence reports, comprising 5,525 document-grounded question-answer pairs spanning factual, inferential, comparative, and risk-oriented questions.

How to run

ltb · geopoliticalqa
$ npm i -g lettokenburn-cli
$ ltb bench info geopoliticalqa

External harness. No Harbor adapter has been generated for this benchmark yet. `ltb bench info` prints the known links.

Runs happen on your machine with your own subscription; only the score summary is pushed. CLI docs

Scores

6 sourced entries
HarnessSourceConfidenceLink
Llama-3.3-70B-Instruct2.6N/AIndependentmediumOct 8, 2026arxiv.org
Mistral-Small-3.22.5N/AIndependentmediumOct 8, 2026arxiv.org
Qwen3-32B2.5N/AIndependentmediumOct 8, 2026arxiv.org
Qwen3 8B2.5N/AIndependentmediumOct 8, 2026arxiv.org
Llama-3.1-8B-Instruct2.3N/AIndependentmediumOct 8, 2026arxiv.org
Mistral NeMo2.3N/AIndependentmediumOct 8, 2026arxiv.org

Every number links to where it was published. Low-confidence rows are muted and need per-cell verification.

Reproduced by the community

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About

The dataset contains 5,525 QA pairs derived from 415 open-source geopolitical reports using an automated, document-aware generation pipeline. It covers diverse institutional sources and question types including factual and risk-oriented queries.

Evaluation involves 15 retrieval systems and six LLMs, assessed via expert annotation and an LLM-as-judge protocol. Findings indicate chunk-level retrieval quality is not driven by embedding scale, while answer generation remains challenging for inferential tasks.