Translator Layer proposal

[IlyaGazman]

Current implementations of Large Language Models (LLMs) must handle a vast array of data inputs across multiple languages including standard languages like Chinese, Russian, and Hebrew, as well as non-intuitive encodings such as Morse code, ASCII graphics, and BASE64. These multifaceted inputs necessitate LLMs to maintain extensive dictionaries, exceeding 100,000 tokens.

To better explain my idea I created a video proposing the incorporation of a translation layer into LLM architectures, aimed at preprocessing all training inputs and referencing both initial queries and outputs in a uniform language (English).

The translation layer significantly condenses the dictionary size, potentially boosting LLM efficiency by orders of magnitude, while maintaining the integrity of the data through advanced translation technologies, hence, minimizing loss of information. Additionally, this layer would fortify the model against security vulnerabilities particularly linked with non-standard language inputs.

This theoretical exploration seeks to highlight the practicality, potential benefits, and consequential enhancements of employing a translator layer in LLMs.

https://youtu.be/UBzqMIhzwWY