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

Eren Unlu; Unver Ciftci

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arXiv · 2310.18390

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

Eren Unlu·Unver Ciftci

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Abstract

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.

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Eren Unlu, Unver Ciftci. 2023-10-27. Entity Embeddings : Perspectives Towards an Omni-Modality Era for Large Language Models. https://arxiv.org/abs/2310.18390

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