Capability-Profiled Agricultural Language-Model Systems: Calibration and Cost-Aware Routing across Memorization, Understanding, Reasoning, and Generation
DOI:
https://doi.org/10.61424/g6rbs789Keywords:
AgriEval; agricultural question answering; capability profiling; calibration; cost-aware routing; retrieval; self-verification; Qwen2.5Abstract
Agricultural language systems must span factual recall, diagnosis, numerical reasoning, and management advice, yet a single score conceals these differences. This study profiles the AgriEval 2025 snapshot using 14,697 multiple-choice and 2,167 open-answer questions across six domains, 29 subdomains, 15 tasks, and four capability families. Duplicate-normalized question groups were assigned to five stratified folds before training, calibration, and held-out testing. Experiments compared Qwen2.5-0.5B-Instruct in direct and self-verification conditions with position, character TF-IDF, case-retrieval, fusion, and answer-copy baselines. On 2,940 multiple-choice test questions, CharPair achieved the highest exact accuracy, 0.3833 (95% confidence interval [0.3653, 0.3997]); Qwen direct reached 0.3286 [0.3119, 0.3459]. Isotonic regression reduced Qwen’s 10-bin expected calibration error from 0.2646 to 0.0147 without changing answers. Self-verification reduced accuracy to 0.2922: 261 errors were corrected, but 368 correct direct answers were corrupted. A calibration-trained CharPair-to-Qwen router sent 9.01% of questions to Qwen, attained 0.3810 accuracy, and required 60.455 ms per question, compared with 0.3833 and 0.549 ms for CharPair alone. On 433 open-answer items, Qwen obtained character ROUGE-L of 0.1555 [0.1477, 0.1636], while VerifiedRAG reached 0.1641. The experiments show that calibration can improve probability reliability even when retrieval, verification, and routing do not improve answer quality. Capability, subgroup, uncertainty, and measured-compute profiles therefore provide a more informative assessment than one leaderboard score.
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