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
概括
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.
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.
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