Predictive Simultaneous Interpretation: Harnessing Large Language Models for Democratizing Real-Time Multilingual Communication
This work addresses the challenge of real-time multilingual communication, aiming to democratize access to interpretation services, though it appears incremental as it builds on existing LLM capabilities.
This paper tackles the problem of simultaneous interpretation by proposing a novel algorithm that uses Large Language Models to predict speaker utterances and generate real-time translations through a tree-like structure, potentially achieving more natural and fluent translations with minimal latency.
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.