随着人工智能技术的发展，大语言模型(large language models，LLMs)已开始在生物多样性研究中发挥重要作用。LLMs的深度学习和自然语言处理技术，结合人类反馈的强化学习(human feedback reinforced learning，RLHF)和近端策略优化(proximal policy optimization，PPO)，提供了处理和分析生物多样性大数据的新途径。本文以Kimi Chat为例探讨了LLMs在探索生物多样性研究问题、文献回顾、设计假设、数据整理与分析及论文写作中的应用。(1)LLMs可以快速处理大量的科学文献，帮助研究人员提炼关键信息，迅速了解特定领域的研究动态。(2)LLMs还可以协助研究人员提出研究假设和设计实验方案，从而提供丰富的科研灵感，拓宽研究思路，提高科研初始阶段的效率。(3)在研究设计方面，LLMs能够提供关于数据收集方法、实验设计和统计分析的建议，确保研究设计的科学性和逻辑性。(4)LLMs在科学写作过程中可以帮助研究人员起草科学论文，还能提供修改和润色建议，提高论文的质量和可读性，优化研究成果的表达。本文也讨论了使用LLMs时遇到的挑战与限制，如专业判断、研究方法的同质化、数据和结果的准确性和伦理问题，并提出了未来融合这种技术与传统生物多样性研究方法的策略。通过实例分析，本文还展示了LLMs如何助力生物多样性和生态科学研究，从而推动科学发现和生态保护策略的发展。
Application of large language models in biodiversity research
Background & Aims:With the development and advancement of artificial intelligence technology,large language models(LLMs),such as Kimi Chat,have begun to play a significant role in biodiversity research.LLMs's deep learning and natural language processing technologies,augmented by human feedback reinforced learning(RLHF)and proximal policy optimization(PPO),offer new avenues for handling and analyzing large biodiversity data sets.Progresses:We explore the application of LLMs,taking Kimi Chat as an example,in investigating biodiversity research questions,reviewing literature,designing hypotheses,organizing and analyzing data,and writing research papers,as well as its potential to enhance research efficiency and quality.(1)LLMs can quickly process vast amounts of scientific literature,helping researchers distill key information and swiftly catch up with the latest research trends in specific fields.(2)LLMs can also assist researchers in formulating research hypotheses and designing experimental protocols,thereby providing abundant scientific inspiration,broadening research perspectives,and enhancing the efficiency of the initial stages of research.(3)In terms of research design,LLMs can offer advice on data collection methods,design of experiment,and statistical analyses to ensure the scientific validity and the logic of the research design.(4)LLMs can assist in scientific writing and peer review processes by helping draft scientific papers and providing suggestions for revision and polishing to enhance the quality and readability of the papers,and it also supports researchers in understanding and responding to peer review comments and optimizing the presentation of research findings.We also discuss the challenges and limitations encountered during using LLMs,such as the need for professional judgment,the homogenization of research methods,the accuracy of data and results,and ethical issues.Additionally,we propose strategies for integrating this technology with traditional biodiversity research methods in the future.Prospects:We demonstrates how LLMs can aid in biodiversity research,thus advancing scientific discovery and ecological conservation strategies.
large language modelsbiodiversity researchscientific writingresearch designresearch methods
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