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相关概念视频

Language Development01:22

Language Development

305
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
305

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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快速帮助:用于大型语言模型的视觉快速探索,扰乱,测试和代.

Aditi Mishra, Bretho Danzy, Utkarsh Soni

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    此摘要是机器生成的。

    PROMPTAID是一个视觉分析系统,可以帮助用户创建和完善大型语言模型 (LLM) 的提示. 它降低了认知负载,并提高了自然语言处理 (NLP) 任务的快速性能.

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    科学领域:

    • 自然语言处理 (NLP) 是一种自然语言处理.
    • 人与计算机的交互 (HCI)
    • 视觉分析 视觉分析 视觉分析

    背景情况:

    • 大型语言模型 (LLM) 是通过自然语言提示程序在临时NLP任务中受欢迎的.
    • 快速变化显著影响LLM任务性能.
    • 非专家在没有领域知识或反的情况下努力优化提示.

    研究的目的:

    • 介绍PROMPTAID,用于交互式提示创建和改进的视觉分析系统.
    • 允许用户在提示时进行探索,扰乱,测试和代.
    • 为非专家用户解决快速优化的挑战.

    主要方法:

    • PROMPTAID使用协调的可视化来实现快速改进.
    • 使用诸如关键字和转述扰动等策略.
    • 包含获取最佳的上下文短暂示例.
    • 设计涉及NLP专家预研究和混合方法用户评估.

    主要成果:

    • 在快速代过程中,PROMPTAID可以减少认知开销.
    • 通过建议促进生成各种提示.
    • 能够有效地分析及时的性能.
    • 性能优于现有的最先进的提示接口.

    结论:

    • PROMPTAID使用户,特别是非专家,能够有效地创建和完善LLM提示.
    • 视觉分析可以显著提高快速工程的可用性和性能.
    • 该系统为NLP任务的交互式提示优化提供了一种新的方法.