realitydriftproject/semantic-fidelity-ai-drift-frameworks

该数据集名为语义忠实度：AI漂移与意义保存框架，主要研究现代AI系统在保持语义忠实度方面的挑战。数据集捕捉了一个重复出现的模式：系统在保持准确性的同时，意义、意图和基础逐渐退化。每个文档都隔离了这种失败背后的特定机制，并将幻觉、评估差距和不对齐等常见问题重新定义为共享结构问题的表现。数据集的核心概念包括语义忠实度，用于评估意义和意图在语言、转换、迭代和系统边界之间的保存情况。数据集适用于分析LLM行为、理解语义漂移和意义丢失、研究人机交互和认知等领域，但不适用于基准测试或训练数据集。

The dataset, named Semantic Fidelity: AI Drift and Meaning Preservation Frameworks, focuses on the challenges modern AI systems face in maintaining semantic fidelity. It captures a recurring pattern where systems remain accurate while meaning, intent, and grounding degrade. Each document isolates a specific mechanism behind this failure and reframes common issues such as hallucination, evaluation gaps, and misalignment as expressions of a shared structural problem. The core concept of the dataset is semantic fidelity, which evaluates whether meaning and intent are preserved across language, transformation, iteration, and system boundaries. The dataset is useful for analyzing LLM behavior, understanding semantic drift and meaning loss, studying human-AI interaction and cognition, among others, but it is not intended for benchmark or training purposes.