PROST-LLM: Progressively Enhancing the Speech-to-Speech Translation Capability in LLMs

• URL: http://arxiv.org/abs/2601.16618v1
• Date: Fri, 23 Jan 2026 10:16:13 GMT
• Title: PROST-LLM: Progressively Enhancing the Speech-to-Speech Translation Capability in LLMs
• Abstract summary: We propose PROST-LLM (PROgressive Speech-to-speech Translation) to enhance the S2ST capabilities in Large Language Models.<n>First, we fine-tune the LLMs with the CVSS corpus, employing designed tri-task learning and chain of modality methods to boost the initial performance.<n>Finally, these preference pairs are used for preference optimization to enhance the model's S2ST capability further.
• Score: 13.891322931352649
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although Large Language Models (LLMs) excel in many tasks, their application to Speech-to-Speech Translation (S2ST) is underexplored and hindered by data scarcity. To bridge this gap, we propose PROST-LLM (PROgressive Speech-to-speech Translation) to enhance the S2ST capabilities in LLMs progressively. First, we fine-tune the LLMs with the CVSS corpus, employing designed tri-task learning and chain of modality methods to boost the initial performance. Then, leveraging the fine-tuned model, we generate preference pairs through self-sampling and back-translation without human evaluation. Finally, these preference pairs are used for preference optimization to enhance the model's S2ST capability further. Extensive experiments confirm the effectiveness of our proposed PROST-LLM in improving the S2ST capability of LLMs.

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