个性化语言模型选择通过对比概念偏好的游戏化诱导
IEEE transactions on visualization and computer graphics
|July 26, 2023
概括
本研究介绍了概念宇宙,这是一种游戏化的视觉分析工具,用于捕捉用户的个人语言理解. 它通过让用户参与概念描述任务来实现个性化的语言模型建议.
科学领域:
- 自然语言处理自然语言处理.
- 人与计算机的交互
- 游戏化就是游戏化.
- 视觉分析 视觉分析 视觉分析
背景情况:
- 语言模型的个性化至关重要,但由于难以获取特定用户的培训数据,因此具有挑战性.
- 现有的方法往往需要大量的用户数据,这可能是困难和耗时的获取.
- 调整模型参数并不是唯一的解决方案;将模型与用户的语言概念心理模型相匹配是一个可行的替代方案.
研究的目的:
- 为了捕捉个人用户对语言概念的心理模型,以提供个性化的语言模型建议.
- 为了应对用户脱离的挑战和语言理解的上下文性质.
- 整合游戏化和视觉分析,以有效地外部化用户知识.
主要方法:
- 开发了概念宇宙,一个游戏化的视觉分析游戏场.
- 用户在一个四阶段的游戏中用关键词解释概念名称.
- 实施了持续的视觉,语言和听觉反机制.
- 集成了一个虚拟对手来增强用户参与度和输入特异性.
主要成果:
- 与六名参与者进行的用户研究表明,用户参与度很高.
- 与基线相比,游戏化方法导致了更具体的用户输入.
- 有效地生成个性化的概念描述.
- 个性化概念被用于语言模型建议.
结论:
- 游戏化视觉分析可以成功地吸引用户将他们的语言心理模型外部化.
- 概念宇宙为个性化的语言模型选择提供了一种新的方法.
- 这种方法提供了一个有前途的替代品,用于个性化的传统微调.
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