AI4Fire: Evaluating Large Language Models on Wildfire Tasks
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Computer Science > Computation and Language
arXiv:2610.10946 (cs)
[Submitted on 7 Oct 2026]
Title:AI4Fire: Evaluating Large Language Models on Wildfire Tasks
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Abstract:Large language models (LLMs) are entering wildfire management, where overstated evaluations can cost property and lives. How do they perform on wildfire tasks, with and without grounding? Bare means a model receives the task input alone. Grounded means it also receives one task-specific addition: for smoke detection, a smoke-free reference frame from the same camera. AI4Fire runs six core models bare and grounded on five wildfire tasks, zero-shot; a sweep adds 29 more. Our literature search on fire tasks found 138 works; none combines this roster, task coverage, and paired bare and grounded runs. We report three findings. (1) Grounding helped most where the addition carried the answer: a read-only SQL tool lifted every core model's database accuracy from at most 16 to at least 88 percent. (2) Simple rules were hard to beat: no core model outperformed repeating today's staffing count, and two open-weight models mostly copied the median of similar earlier fire-days, a worse forecast. (3) Public releases carry hazards: a fire-danger column separates the holdout perfectly, and 67 aerial fire frames carry smoldering or fire-free labels read from a clipped thermal maximum. We release prompts, responses, scores, code, and the survey record.
| Comments: | 51 pages. Code: this https URL |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.10946 [cs.CL] |
| (or arXiv:2610.10946v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10946
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled AI4Fire: Evaluating Large Language Models on Wildfire Tasks, by Yue Zhao and 4 other authors
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