Predictive Simultaneous Interpretation: Harnessing Large Language Models for Democratizing Real-Time Multilingual Communication

TL;DR

This paper proposes a novel approach to simultaneous interpretation using Large Language Models, predicting speaker utterances to generate real-time, flexible translations, aiming to improve naturalness and reduce latency in multilingual communication.

Contribution

Introduces a new algorithm leveraging LLMs for real-time translation prediction, enhancing flexibility and overcoming language structural differences.

Findings

Demonstrates potential for more natural, fluent translations

Shows reduced latency compared to traditional systems

Provides a theoretical foundation for future development

Abstract

This study introduces a groundbreaking approach to simultaneous interpretation by directly leveraging the predictive capabilities of Large Language Models (LLMs). We present a novel algorithm that generates real-time translations by predicting speaker utterances and expanding multiple possibilities in a tree-like structure. This method demonstrates unprecedented flexibility and adaptability, potentially overcoming the structural differences between languages more effectively than existing systems. Our theoretical analysis, supported by illustrative examples, suggests that this approach could lead to more natural and fluent translations with minimal latency. The primary purpose of this paper is to share this innovative concept with the academic community, stimulating further research and development in this field. We discuss the theoretical foundations, potential advantages, and implementation challenges of this technique, positioning it as a significant step towards democratizing multilingual communication.