[2605.04177] Are LLMs Ready for Conflict Monitoring? Empirical Evidence from West Africa
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Computer Science > Computation and Language
arXiv:2605.04177 (cs)
[Submitted on 5 May 2026]
Title:Are LLMs Ready for Conflict Monitoring? Empirical Evidence from West Africa
Authors:Hoffmann Muki, Olukunle Owolabi
View a PDF of the paper titled Are LLMs Ready for Conflict Monitoring? Empirical Evidence from West Africa, by Hoffmann Muki and 1 other authors
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Abstract:As LLMs enter conflict monitoring, understanding systematic distortions in their outputs is critical for humanitarian accountability. We evaluate four vanilla open-weight models Gemma 3 4B, Llama 3.2 3B, Mistral 7B, and OLMo 2 7B and two domain-adapted models, AfroConfliBERT and AfroConfliLLAMA, on Nigeria and Cameroon conflict-event classification against ACLED, a gold-standard dataset with multi-stage verification. We find a bifurcated divergence in normative directionality. Open-weight models exhibit statistically significant False Illegitimation bias: Gemma misclassifies to 18.29% of legitimate battles as civilian-targeted violence while making zero False Legitimation errors. By contrast, AfroConfliBERT and AfroConfliLLAMA achieve near-directional neutrality, with Legitimization Bias differences indistinguishable from zero. Yet domain adaptation does not eliminate actor-based selection bias. Both adapted models show statistically significant actor bias comparable to vanilla LLMs; in Nigeria, state actors are legitimized 36.5% more often than non-state actors in identical tactical contexts. Open-weight outputs are also fragile to geography-specific lexical framing: delegitimizing phrases produce flip rates up to 66.7% in Cameroon and 34.2% in Nigeria, while perturbations salient in one context may not matter in another. Error trace profiling shows models mask normative bias through unfaithful rationale confabulations. In contrast, AfroConfliBERT and AfroConfliLLAMA are largely robust, with near-zero flip rates across perturbation categories. Overall, current models are not ready for unsupervised deployment in conflict monitoring. We call for fairness-aware fine-tuning to reduce actor-based selection bias, mandatory adversarial robustness evaluation against lexical manipulation, and context-specific human-in-the-loop oversight calibrated to regional difficulty.
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2605.04177 [cs.CL]
(or arXiv:2605.04177v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.04177
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arXiv-issued DOI via DataCite (pending registration)
Related DOI:
https://doi.org/10.1145/3805689.3812264
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From: Hoffmann Muki [view email] [v1] Tue, 5 May 2026 18:14:59 UTC (1,418 KB)
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