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

Eren Unlu, Unver Ciftci
10/27/2023
cs.LG

No confident code implementation found for this paper. 2 possible matches can be shown below.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{unlu2023entity,
  title  = {Entity Embeddings : Perspectives Towards an Omni-Modality Era for Large Language Models},
  author = {Eren Unlu and Unver Ciftci},
  year   = {2023},
  eprint = {2310.18390},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.18390v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2310.18390.svg)](https://codeofpaper.com/paper/2310.18390)

Discussion