Text Endpoint Detection Based on Large Language Models

This paper introduces a method to detect text endpoints using large language models (LLMs) to improve Voice Activity Detection (VAD) in voice conversations. By training a fine-tuned model to predict whether a sentence is complete, the user's intent can be more accurately judged. The specific steps include: 1. **Principle and Data Preparation**: Leverage the text generation capabilities of large language models to fine-tune based on predefined datasets and specific formats. 2. **Fine-tuning the Model**: Use the LLaMA-Factory tool for training, selecting appropriate prompt templates and optimized data formats. 3.