[1] |
BROWN T, MANN B, RYDER N, et al. Language models are few-shot learners[J]. Advances in neural information processing systems, 2020, 33: 1877-1901.
|
[2] |
张振乾, 汪澍, 宋琦, 等. 人工智能大模型在智慧农业领域的应用[J]. 智慧农业导刊, 2023, 3(10):9-12+17.
|
[3] |
郭旺, 杨雨森, 吴华瑞, 等. 农业垂直领域大语言:关键技术、应用分析与发展方向[J]. 智慧农业(中英文), 2024, 6(2):1-13.
|
[4] |
VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30.
|
[5] |
WANG Y, ZHANG Z, WANG R. Element-aware summarization with large language models: Expert-aligned evaluation and chain-of- thought method[J]. arXiv preprint arXiv:2305.13412, 2023.
|
[6] |
BRIAKOU E, CHERRY C, FOSTER G. Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM's Translation Capability[J]. arXiv preprint arXiv:2305.10266, 2023.
|
[7] |
LIU Z, YANG K, ZHANG T, et al. Emollms: A series of emotional large language models and annotation tools for comprehensive affective analysis[J]. arXiv preprint arXiv:2401.08508, 2024.
|
[8] |
王婷, 王娜, 崔运鹏, 等. 基于人工智能大模型技术的果蔬农技知识智能问答系统[J]. 智慧农业(中英文), 2023, 5(4):105-116.
|
[9] |
ZHANG X, TIAN C, YANG X, et al. Alpacare: Instruction-tuned large language models for medical application[J]. arXiv preprint arXiv: 2310. 14558, 2023.
|
[10] |
ZHANG H, CHEN J, JIANG F, et al. HuatuoGPT, towards taming language model to be a doctor[OL]. arXiv preprint, 2023. arXiv: 2305.15075.
|
[11] |
HUANG Q, TAO M, AN Z, et al. Lawyer llama technical report[J/OL]. arXiv preprint, 2023. arXiv:2305.15062.
|
[12] |
LUO Y, ZHANG J, FAN S, et al. Biomedgpt: Open multimodal generative pre-trained transformer for biomedicine[J/OL]. arXiv preprint, 2023. arXiv:2308.09442.
|
[13] |
LUO Y, YANG K, HONG M, et al. Molfm: A multimodal molecular foundation model[J/OL]. arXiv preprint, 2023. arXiv:2307.09484.
|
[14] |
ZHAO S, ZHANG J, NIE Z. Large-scale cell representation learning via divide-and-conquer contrastive learning[J/OL]. arXiv preprint, 2023. arXiv:2306.04371.
|
[15] |
YANG H, LIU X Y, WANG C D. Fingpt: Open-source financial large language models[J/OL]. arXiv preprint, 2023. arXiv:2306.06031.
|
[16] |
LI Y, MA S, WANG X, et al. EcomGPT: Instruction-tuning large language models with chain-of-task tasks for e-commerce[C]// Proceedings of the AAAI Conference on Artificial Intelligence. 2024, 38(17): 18582-18590.
|
[17] |
YANG X, GAO J, XUE W, et al. Pllama: An open-source large language model for plant science[J]. arXiv preprint arXiv:2401.01600, 2024.
|
[18] |
ZHAO B, JIN W, SER J D, et al. ChatAgri: Exploring potentials of ChatGPT on cross-linguistic agricultural text classification[J]. Neurocomputing, 2023, 557: 126708.
|
[19] |
BALAGUER A, BENARA V, DE FREITAS CUNHA R L, et al. RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture[J/OL]. arXiv e-prints, 2024. arXiv: 2401.08406.
|
[20] |
SILVA B, NUNES L, ESTEVÃO R, et al. GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models[J]. arXiv preprint, 2023. arXiv:2310.06225.
|
[21] |
WANG Y, KORDI Y, MISHRA S, et al. Self-instruct: Aligning language models with self-generated instructions[J/OL]. arXiv preprint, 2022. arXiv:2212.10560.
|
[22] |
ZHANG X, YANG Q. Self-qa: Unsupervised knowledge guided language model alignment[J/OL]. arXiv preprint, 2023. arXiv:2305. 11952.
|
[23] |
LIU X, HONG H, WANG X, et al. Selfkg: Self-supervised entity alignment in knowledge graphs[C]// Proceedings of the ACM Web Conference 2022. 2022: 860-870.
|
[24] |
YAN J, WANG C, CHENG W, et al. A retrospective of knowledge graphs[J]. Frontiers of Computer Science, 2018, 12: 55-74.
doi: 10.1007/s11704-016-5228-9
|
[25] |
侯琛, 牛培宇. 农业知识图谱技术的研究现状与展望[J]. 农业机械学报, 2024, 55(6):1-17.
|
[26] |
田鹏菲. 苹果病虫害知识图谱和施药辅助App的研究与实现[D]. 新疆塔里木: 塔里木大学, 2023.
|
[27] |
张宇, 郭文忠, 林森, 等. 基于Neo4j的草莓种植管理知识图谱构建及验证[J]. 现代农业科技, 2022,(1):223-230+234.
|
[28] |
张文豪. 基于大豆育种语料的知识图谱构建[D]. 济南: 山东大学, 2021.
|
[29] |
TOUVRON H, LAVRIL T, IZACARD G, et al. Llama: Open and efficient foundation language models[J/OL]. arXiv preprint, 2023. arXiv:2302.13971.
|
[30] |
TOUVRON H, MARTIN L, STONE K, et al. Llama 2: Open foundation and fine-tuned chat models[J/OL]. arXiv preprint, 2023. arXiv:2307.09288.
|
[31] |
WORKSHOP B S, SCAO T L, FAN A, et al. Bloom: A 176b- parameter open-access multilingual language model[J/OL]. arXiv preprint, 2022. arXiv:2211.05100.
|
[32] |
DU N, HUANG Y, DAI A M, et al. Glam: Efficient scaling of language models with mixture-of-experts[C]// International Conference on Machine Learning. PMLR, 2022: 5547-5569.
|
[33] |
CHOWDHERY A, NARANG S, DEVLIN J, et al. Palm: Scaling language modeling with pathways[J]. Journal of Machine Learning Research, 2023, 24(240): 1-113.
|
[34] |
BAI J, BAI S, CHU Y, et al. Qwen technical report[J/OL]. arXiv preprint, 2023. arXiv:2309.16609.
|
[35] |
DU Z, QIAN Y, LIU X, et al. Glm: General language model pretraining with autoregressive blank infilling[J/OL]. arXiv preprint, 2021. arXiv:2103.10360.
|
[36] |
YANG A, XIAO B, WANG B, et al. Baichuan 2: Open large-scale language models[J/OL]. arXiv preprint, 2023. arXiv:2309.10305.
|
[37] |
OUYANG L, WU J, JIANG X, et al. Training language models to follow instructions with human feedback[J]. Advances in Neural Information Processing Systems, 2022, 35: 27730-27744.
|
[38] |
LIU H, TAM D, MUQEETH M, et al. Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning[J]. Advances in Neural Information Processing Systems, 2022, 35: 1950-1965.
|
[39] |
LESTER B, AL-RFOU R, CONSTANT N. The power of scale for parameter-efficient prompt tuning[J/OL]. arXiv preprint, 2021. arXiv:2104.08691.
|
[40] |
HU E J, SHEN Y, WALLIS P, et al. Lora: Low-rank adaptation of large language models[J/OL]. arXiv preprint, 2021. arXiv:2106.09685.
|
[41] |
LI X L, LIANG P. Prefix-tuning: Optimizing continuous prompts for generation[J/OL]. arXiv preprint, 2021. arXiv:2101.00190.
|
[42] |
LIU X, JI K, FU Y, et al. P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks[J/OL]. arXiv preprint, 2021. arXiv:2110.07602.
|
[43] |
LEWIS P, PEREZ E, PIKTUS A, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks[J]. Advances in Neural Information Processing Systems, 2020, 33: 9459-9474.
|
[44] |
GAO Y, XIONG Y, GAO X, et al. Retrieval-augmented generation for large language models: A survey[J/OL]. arXiv preprint, 2023. arXiv:2312.10997.
|
[45] |
CHANG Y, WANG X, WANG J, et al. A survey on evaluation of large language models[J]. ACM Transactions on Intelligent Systems and Technology, 2024, 15(3): 1-45.
|
[46] |
HENDRYCKS D, BURNS C, BASART S, et al. Measuring massive multitask language understanding[J/OL]. arXiv preprint, 2020. arXiv:2009.03300.
|
[47] |
LEWKOWYCZ A, SLONE A, ANDREASSEN A, et al. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models[R]. Technical Report, 2022.
|
[48] |
BOMMASANI R, LIANG P, LEE T. Holistic evaluation of language models[J]. Annals of the New York Academy of Sciences, 2023, 1525(1): 140-146.
|
[49] |
CHIANG W L, ZHENG L, SHENG Y, et al. Chatbot arena: An open platform for evaluating llms by human preference[J/OL]. arXiv preprint, 2024. arXiv:2403.04132.
|
[50] |
ZHANG N, CHEN M, BI Z, et al. Cblue: A chinese biomedical language understanding evaluation benchmark[J/OL]. arXiv preprint, 2021. arXiv:2106.08087.
|
[51] |
FEI Z, SHEN X, ZHU D, et al. Lawbench: Benchmarking legal knowledge of large language models[J/OL]. arXiv preprint, 2023. arXiv:2309.16289.
|
[52] |
GU Z, ZHU X, YE H, et al. Xiezhi: An ever-updating benchmark for holistic domain knowledge evaluation[C]// Proceedings of the AAAI Conference on Artificial Intelligence. 2024, 38(16): 18099-18107.
|
[53] |
CHIANG C H, LEE H. Can large language models be an alternative to human evaluations?[J/OL]. arXiv preprint, 2023. arXiv:2305.01937.
|
[54] |
WEI J, WANG X, SCHUURMANS D, et al. Chain-of-thought prompting elicits reasoning in large language models[J]. Advances in Neural Information Processing Systems, 2022, 35: 24824-24837.
|
[55] |
LIU Y, ITER D, XU Y, et al. G-eval: Nlg evaluation using gpt-4 with better human alignment[J]. arXiv preprint, 2023. arXiv: 2303.16634.
|
[56] |
PIÑEIRO-MARTÍN A, GARCÍA-MATEO C, DOCÍO-FERNÁNDEZ L, et al. Ethical challenges in the development of virtual assistants powered by large language models[J]. Electronics, 2023, 12(14): 3170.
|
[57] |
SHUTSKE J M. Harnessing the power of large language models in agricultural safety & health[J]. Journal of Agricultural Safety and Health, 2023: 0.
|
[58] |
CHEN X, LI L, CHANG L, et al. Challenges and Contributing Factors in the Utilization of Large Language Models (LLMs)[J/OL]. arXiv preprint, 2023. arXiv:2310.13343.
|
[59] |
McCLOSKEY M, COHEN N J. Catastrophic interference in connectionist networks: The sequential learning problem[M]// Psychology of Learning and Motivation. Academic Press, 1989, 24: 109-165.
|
[60] |
JI Z, LEE N, FRIESKE R, et al. Survey of hallucination in natural language generation[J]. ACM Computing Surveys, 2023, 55(12):1-38.
|
[61] |
HUANG L, YU W, MA W, et al. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions[J/OL]. arXiv preprint, 2023. arXiv:2311.05232.
|
[62] |
MIN S, KRISHNA K, LYU X, et al. Factscore: Fine-grained atomic evaluation of factual precision in long form text generation[J/OL]. arXiv preprint, 2023. arXiv:2305.14251.
|
[63] |
ELARABY M, LU M, DUNN J, et al. Halo: Estimation and reduction of hallucinations in open-source weak large language models[J/OL]. arXiv preprint, 2023. arXiv:2308.11764.
|
[64] |
TAN C, CAO Q, LI Y, et al. On the promises and challenges of multimodal foundation models for geographical, environmental, agricultural, and urban planning applications[J/OL]. arXiv preprint, 2023. arXiv:2312.17016.
|
[65] |
YANG Z, LI L, LIN K, et al. The dawn of LMMs: Preliminary explorations with GPT-4V (Ision)[J/OL]. arXiv preprint, 2023. arXiv: 2309.17421.
|
[66] |
IBRAHIM A, SENTHILKUMAR K, SAITO K. Evaluating responses by ChatGPT to farmers’ questions on irrigated rice cultivation in Nigeria[J]. Research Square Platform LLC, 2023.
|
[67] |
杜保佳, 张晶, 王宗明, 等. 应用 Sentinel-2A NDVI 时间序列和面向对象决策树方法的农作物分类[J]. 地球信息科学学报, 2019, 21(5): 740-751.
doi: 10.12082/dqxxkx.2019.180412
|
[68] |
陈诗扬, 刘佳. 基于 GF-6 时序数据的农作物深度学习识别算法评估[J]. 农业工程学报, 2021, 37(15):161-168.
|
[69] |
ABRAMSON J, ADLER J, DUNGER J, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3[J]. Nature, 2024: 1-3.
|
[70] |
CHEN L, ZAHARIA M, ZOU J. Frugalgpt: How to use large language models while reducing cost and improving performance[J/OL]. arXiv preprint, 2023. arXiv:2305.05176.
|