Embeddings have become a pivotal means to represent complex, multi-faceted information about entities, concepts, and relationships in a condensed and useful format. Nevertheless, they often preclude direct interpretation. While downstream tasks make use of these compressed representations, meaningful interpretation usually requires visualization using dimensionality reduction or specialized machine learning interpretability methods. This paper addresses the challenge of making such embeddings more interpretable and broadly useful, by employing Large Language Models (LLMs) to directly interact with embeddings -- transforming abstract vectors into understandable narratives. By injecting embeddings into LLMs, we enable querying and exploration of complex embedding data. We demonstrate our approach on a variety of diverse tasks, including: enhancing concept activation vectors (CAVs), communicating novel embedded entities, and decoding user preferences in recommender systems. Our work couples the immense information potential of embeddings with the interpretative power of LLMs.

翻译：嵌入已成为以紧凑且实用的形式表征实体、概念及关系间复杂多维信息的关键手段。然而，嵌入往往阻碍直接解读。尽管下游任务利用这些压缩表示，但有意义的解释通常需借助降维可视化或专门的机器学习可解释性方法。本文通过采用大型语言模型（LLMs）直接与嵌入交互——将抽象向量转化为可理解的叙述——解决使嵌入更具可解释性和广泛实用性的挑战。通过将嵌入注入LLMs，我们得以查询和探索复杂的嵌入数据。我们在多种不同任务中展示了该方法，包括：增强概念激活向量（CAVs）、传达新型嵌入实体，以及解码推荐系统中的用户偏好。本研究将嵌入的巨大信息潜力与LLMs的阐释能力相结合。