Large Language Models (LLMs) are evolving to integrate multiple modalities,
such as text, image, and audio into a unified linguistic space. We envision a
future direction based on this framework where conceptual entities defined in
sequences of text can also be imagined as modalities. Such a formulation has
the potential to overcome the cognitive and computational limitations of
current models. Several illustrative examples of such potential implicit
modalities are given. Along with vast promises of the hypothesized structure,
expected challenges are discussed as well.

大型语言模型（LLMs）正在发展，将多种模态（如文本、图像和音频）整合到统一的语言空间中。我们预见到基于此框架的未来方向，其中在文本序列中定义的概念实体也可以被想象为模态。这种表述有潜力克服当前模型的认知和计算限制。给出了几个这种潜在隐含模态的例子。除了对猜想结构的广泛承诺，还讨论了预期的挑战。

实体嵌入：大型语言模型朝向全模态时代的视角

Entity Embeddings : Perspectives Towards an Omni-Modality Era for Large  Language Models

Topic models aim to reveal the latent structure behind a corpus, typically
conducted over a bag-of-words representation of documents. In the context of
topic modeling, most vocabulary is either irrelevant for uncovering underlying
topics or contains strong relationships with relevant concepts, impacting the
interpretability of these topics. Furthermore, their limited expressiveness and
dependency on language demand considerable computation resources. Hence, we
propose a novel approach for cluster-based topic modeling that employs
conceptual entities. Entities are language-agnostic representations of
real-world concepts rich in relational information. To this end, we extract
vector representations of entities from (i) an encyclopedic corpus using a
language model; and (ii) a knowledge base using a graph neural network. We
demonstrate that our approach consistently outperforms other state-of-the-art
topic models across coherency metrics and find that the explicit knowledge
encoded in the graph-based embeddings provides more coherent topics than the
implicit knowledge encoded with the contextualized embeddings of language
models.

我们提出了一种基于聚类的主题建模方法，使用概念实体作为语言无关的表示，并使用图神经网络从百科全书语料库和知识库中提取实体的向量表示，该方法在连贯性度量方面始终优于其他主题模型。