解决大模型同时翻译中的位置错位问题，提升效率与一致性。
ExPosST: Explicit Positioning with Adaptive Masking for LLM-Based Simultaneous Machine Translation
- 显式预留位置槽，兼容多种编码方式，支持高效解码。
- 在多个语种对上实现不同策略下的稳定同步翻译性能。
- 适合需要高效且一致的实时翻译系统开发者使用。
大语言模型（LLMs）在同时性机器翻译（SimulMT）中展现出良好性能。然而，将仅解码器的LLM应用于SimulMT会引发位置不匹配问题，导致解码效率与位置一致性之间的权衡。现有方法通常依赖特定的位置编码或精心设计的提示方案，难以同时兼顾推理效率、位置一致性与广泛模型兼容性。本文提出ExPosST，一种通过显式位置分配解决该难题的通用框架。ExPosST为输入源词元预留固定位置槽，支持跨不同位置编码方法的高效解码与KV缓存。为进一步弥合微调与推理间的差距，引入策略一致的微调策略，使训练行为与推理时解码行为保持一致。在多个语言对上的实验表明，ExPosST在不同策略下均能有效支持同步翻译。
原文摘要 · Abstract (English)
Large language models (LLMs) have recently demonstrated promising performance in simultaneous machine translation (SimulMT). However, applying decoder-only LLMs to SimulMT introduces a positional mismatch, which leads to a dilemma between decoding efficiency and positional consistency. Existing approaches often rely on specific positional encodings or carefully designed prompting schemes, and thus fail to simultaneously achieve inference efficiency, positional consistency, and broad model compatibility. In this work, we propose ExPosST, a general framework that resolves this dilemma through explicit position allocation. ExPosST reserves fixed positional slots for incoming source tokens, enabling efficient decoding with KV cache across different positional encoding methods. To further bridge the gap between fine-tuning and inference, we introduce a policy-consistent fine-tuning strategy that aligns training with inference-time decoding behavior. Experiments across multiple language pairs demonstrate that ExPosST effectively supports simultaneous translation under diverse policies.
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