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Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes.
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Within the human body, a complex and detailed system of trillions of cells works in unison to sustain life. Each cell houses a nucleus, which contains 46 chromosomes divided into 23 pairs. Chromosomes are highly coiled structures made of the genetic material DNA. These chromosomes are essential carriers of genetic information, with half inherited from the mother through her egg and the other half from the father's sperm, combining to create the unique genetic makeup of an individual.
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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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从西班牙文学中利用生成型LLM提取遗传实体的探索.

Milindi Kodikara, Karin Verspoor

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    生成型大语言模型 (LLM) 显示出从科学文本中提取遗传和疾病信息的前景. 虽然跨语言提取是可行的,但LLM有时会幻觉数据,但接地输出减少了这个问题.

    科学领域:

    • 生物医学信息学 生物医学信息学
    • 自然语言处理自然语言处理.
    • 基因组学就是基因组学.

    背景情况:

    • 从文献中组织基因,基因变异和疾病信息对于精准医学至关重要.
    • 科学文献的广性需要自动化信息提取策略.
    • 生成型大语言模型 (LLM) 为自动化这种提取过程提供了一个潜在的解决方案.

    研究的目的:

    • 系统地评估LLM在从生物医学文献中提取遗传变异-疾病影响信息方面的表现.
    • 在西班牙语摘要中评估遗传学和疾病命名实体识别 (NER) 的挑战.
    • 探索各种提示策略,以优化LLM提取精度和减少幻觉.

    主要方法:

    • 利用GenoVarDis数据集对西班牙科学摘要的LLMs进行评估.
    • 实验了零射击,少数射击和跨语言提示策略.
    • 研究了注释准则和输出格式变化的对LLM绩效的影响.
    • 评估了减少LLM产生的幻觉的方法,包括提供示例和输出结构概述.

    主要成果:

    • 提示语言对NER性能的影响最小,证实了跨语言提取的可行性.
    • 短暂的提示通常为信息提取提供了最佳的结果.

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  • LLM 倾向于过度生成或制造 (幻觉) 在源文本中不存在的实体.
  • 提供示例和输出结构指导,提示显著减少幻觉实体.
  • 结论:

    • 在生物医学文献中,LLM显示出信息提取的潜力,特别是在少数射击提示和接地技术中.
    • 虽然LLM还不能与特定任务的模型准确度相匹配,但有效的提示策略可以提高其实用性.
    • 在原始文本中将LLM输出接地为确保准确性和减轻生物医学数据提取中的幻觉至关重要.