GBNP-2026：面向畜禽遗传、育种、营养与生产的大语言模型专业问答能力评测基准

doi:10.19543/j.cnki.1673-4556.20260507.001

Abstract

Abstract:

Objective Livestock-related QA is terminology-heavy and often involves indicators, parameters, and trade-offs in breeding and production plans. This study evaluates LLM professional usability using subjective responses and explicit scoring rubrics. Methods We built a Chinese vertical benchmark, the Genetics, Breeding, Nutrition, and Production Benchmark (GBNP-2026), with 510 subjective items (363 short-answer and 147 essay) spanning the same four domains, each with a reference answer, checkable scoring points, and domain tags. Nine open- and closed-source models were tested under a unified zero-shot, context-free protocol, scored by coverage and quality, followed by error attribution on low-score cases. Results Mean scores ranged from 64.03 to 83.99 and coverage from 70.6% to 91.9%. Essay items scored higher than short-answer items on average; breeding was the weakest domain for most models. Among 628 low-score samples, missing knowledge (37.3%) and hallucinations (25.0%) dominated. Missing-knowledge rates differed across domains (χ²=8.42, P = 0.038), highest in breeding (47.1%). Conclusion Compared with multiple-choice accuracy alone, coverage-plus-quality scoring helps separate capability gaps in this setting; strictly numerical or proof-style tasks still need a dedicated sub-benchmark. GBNP-2026 and the pipeline support reproducible comparison, error diagnosis, and data planning for SFT/RAG.

Key words: Large language model, Question bank assessment, Genetic breeding, Automatic scoring, Domain evaluation

CLC Number:

Chengpeng Xiao, Chenglin Li, Yanru Lei, Zhao Cai, Bofei Xie, Kejun Wang, Yujie Guo, Yujie Gong, Donghua Li, Weiwei Jin, Guirong Sun, Yan Luo, Xinjian Gu, Mingzhou Lu, Xiangtao Kang, Wenting Li. GBNP-2026: A benchmark for evaluating large language models' professional question-answering capabilities in animal genetics, breeding, nutrition, and production[J].Chinese Livestock and Poultry Breeding, 2026, 22(7): 17-27.

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基准名称Benchmark name	领域Field	题型Question type	语言Language	评测方式Evaluation method
MMLU	通用57科	选择题	英文	准确率
C-Eval	通用52科	选择题	中文	准确率
MedQA	医学	选择题	英/中	准确率
LawBench	法律	混合	中文	准确率/F1/ROUGE-L/回归距离等
AGIEval	综合考试	选择+填空	中/英	准确率/Exact Match
GBNP-2026（本文）	遗传、育种、营养、生产	简答+论述	中文	LLM评分

模型Model	厂商Manufacturer	类型Type	上下文长度Sequence length	特点Features
GPT-5.2	OpenAI	闭源	~128 K	综合能力最强
Kimi-2.5	Moonshot	开源	~256 K	中文长文本优化
Gemini-3 Pro	Google	闭源	~1 M	多模态原生支持
Claude Sonnet-4.5	Anthropic	闭源	~200 K	安全对齐优秀
GLM-4.7	智谱AI	闭源	~128 K	中文理解优化
DeepSeek-Reasoner(R1)	DeepSeek	开源	~128 K	推理链增强
Grok-4	xAI	闭源	~131 K	实时信息检索
Qwen-Max	阿里巴巴	开源	~256 K	多语言支持
Qwen3-32B	阿里巴巴	开源	~128 K	开源标杆

错误类型 Error type	定义 Definition	遗传Genetics (n=168)/%	营养Nutrition (n=187)/%	生产Production (n=152)/%	育种Breeding (n=121)/%	χ²	P值P-value
知识遗漏Missing knowledge	模型未涉及题目核心要点，知识库中缺乏相关内容	31.50	35.80	38.20	47.10	8.42	0.038
幻觉编造Hallucination	模型生成了事实性错误内容，如捏造概念或数据	29.20	23.50	25.70	21.50	3.21	0.361
答非所问Irrelevant response	模型回答了与题目无关内容，常见于术语歧义	19.60	17.60	14.50	12.40	4.15	0.246
逻辑错误Logical error	模型推理步骤或因果关系出现错误	10.10	11.80	13.80	16.50	3.87	0.275
表述模糊Ambiguous statement	模型回答不够深入，仅提到关键词未展开	7.10	8.60	5.30	2.50	6.23	0.101
其他Others	无法明确归类的错误	2.40	2.70	2.60	0.00	-	-

错误类型Error type	问题示例 Example problem	模型错误回答摘要 Summary of the model's erroneous answers	标准答案要点 Key points of the standard answer	失分原因 Reasons for point loss
知识遗漏 Missing Knowledge	简述BLUP育种值估计的基本原理	仅提到“最佳线性无偏预测”名词	Henderson混合模型方程、亲缘矩阵、固定效应与随机效应分离	缺乏数量遗传学深度知识
	什么是基因组选择（GS）中的参考群体？	回答了GWAS的参考群体概念	具有表型和基因型数据的个体集合、用于训练预测模型	混淆GS与GWAS概念
	列举3种常用的禽类DNA分子标记	SSR、AFLP、SNP	还应包括微卫星标记、RFLP、STR等传统标记	只答新型标记，遗漏经典标记
幻觉编造Hallucination	猪的主要经济性状遗传力范围	“产仔数遗传力0.6～0.8”	产仔数遗传力约0.1～0.15，属于低遗传力性状	严重高估，与教科书相悖
	什么是杂种优势的分子机制？	“杂种优势主要由显性效应导致”	显性假说、超显性假说、上位性假说，机制尚无定论	将假说当作定论
	简述鸡的性别决定机制	“与哺乳动物相同，雄性为XY”	鸡为ZW型，雌性为异配ZW，雄性为同配ZZ	基础知识错误
答非所问 Irrelevant response	解释遗传漂变对小群体的影响	详细论述了自然选择的作用	等位基因频率随机波动、纯合度增加、遗传多样性丧失	完全偏题
答非所问 Irrelevant response	什么是近交系数？	回答了亲缘系数的定义	个体2个等位基因来自共同祖先的概率	混淆F与R

GBNP-2026: A benchmark for evaluating large language models' professional question-answering capabilities in animal genetics, breeding, nutrition, and production

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