Self-Speculative Biased Decoding for Faster Re-translation

Large language models achieve strong machine translation quality but incur high inference cost and latency, posing challenges for simultaneous translation. Re-translation provides a practical solution for off-the-shelf LLMs by repeatedly regenerating the target output as the source input grows, but it suffers from substantial redundant computation. We propose Self-Speculative Biased Decoding (SSBD), a simple and tuning-free inference method that accelerates re-translation by exploiting temporal coherence in streaming translation. SSBD reuses the model’s previous output as a speculative draft for the updated input, verifies the draft efficiently in a single forward pass with a lightweight bias, and resumes autoregressive decoding only from the first divergence. We further introduce a display-only masking strategy that hides unstable suffixes from the user interface while retaining them in the draft for verification and potential acceptance. Across simultaneous translation benchmarks, SSBD yields up to 1.7$\times$ speedup over standard re-translation while preserving comparable translation quality and improving output stability. Because SSBD requires no model changes, auxiliary draft models, or extra fine-tuning, it can be integrated directly into existing LLM-based streaming translation systems, without architectural changes, auxiliary models, or extra fine-tuning.