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相关概念视频

The Anchoring-and-Adjustment Heuristic01:25

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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The Availability Heuristic01:08

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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Heuristics01:21

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
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The Representativeness Heuristic02:13

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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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先知:促使大型语言模型与辅助答案启发式用于基于知识的视觉问题答案.

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    此摘要是机器生成的。

    先知通过提示具有答案启发式的大型语言模型 (LLM) 来增强基于知识的视觉问题答案 (VQA). 这一框架提高了LLM对视觉数据的理解,以获得更准确的VQA性能.

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    科学领域:

    • 人工智能的人工智能
    • 计算机视觉 计算机视觉
    • 自然语言处理自然语言处理.

    背景情况:

    • 基于知识的视觉问题答案 (VQA) 传统上依赖于明确的知识基础,往往导致由于无关紧要信息而导致性能限制.
    • 最近的方法利用大型语言模型 (LLM) 作为隐含的知识引擎,但由于提示中视觉上下文不足,它们的全部潜力尚未得到充分利用.

    研究的目的:

    • 介绍Prophet,一个新的框架,旨在通过结合答案启发式来优化LLM提醒基于知识的VQA.
    • 增强LLM利用视觉信息和外部知识的能力,以获得更准确的VQA.

    主要方法:

    • 在没有外部知识的情况下训练一个基本的VQA模型.
    • 从训练的VQA模型中提取补充的答案启发式 (候选人和答案意识示例).
    • 将这些启发式信息编码成一个格式化的提示符,以指导大型语言模型 (LLM) 回答VQA问题.

    主要成果:

    • 当与GPT-3集成时,Prophet在四个具有挑战性的基于知识的VQA数据集上显著超越现有的最先进方法.
    • 该框架表现出普遍性,支持各种VQA模型和LLM,包括商业和开源选项.

    结论:

    • 先知提供了一种灵活和有效的方法,通过启发式引导提示来增强LLM的理解来提高基于知识的VQA.
    • 先知++ 扩展显示了通过与大型多式联运模式集成进一步进步的潜力.