In the publication Tuning Large language model for End-to-end Speech Translation, Zhang and colleagues introduce the LST model designed to improve end-to-end speech translation (E2E-ST). The model incorporates a speech frontend, an adapter, and an LLM backend, undergoing a two-stage training process to optimize multi-modal translation tasks.

• LST model specifically tuned for E2E-ST task.
• Combines speech recognition and LLMs for cross-language, cross-modal translation.
• Surpasses prior models in BLEU scores on the MuST-C speech translation benchmark.
• Provides groundwork for future research on model selection and training strategies.

This paper represents a significant step in fine-tuning LLMs for complex multimodal translation tasks, showcasing the capacity to break down barriers in human-machine communication.