Rare words remain a critical bottleneck for speech-to-text systems. While direct fine-tuning improves recognition of target words, it often incurs high cost, catastrophic forgetting, and limited scalability. To address these challenges, we propose a training-free paradigm based on task vectors for rare word recognition and translation. By defining task vectors as parameter differences and introducing word-level task vector arithmetic, our approach enables flexible composition of rare-word capabilities, greatly enhancing scalability and reusability. Extensive experiments across multiple domains show that the proposed method matches or surpasses fine-tuned models on target words, improves general performance by about 5 BLEU, and mitigates catastrophic forgetting.

翻译：罕见词仍然是语音转文本系统的关键瓶颈。虽然直接微调能提升目标词的识别能力，但通常伴随着高成本、灾难性遗忘和可扩展性受限等问题。为应对这些挑战，我们提出一种基于任务向量的免训练范式，用于罕见词识别与翻译。通过将任务向量定义为参数差值并引入词级任务向量运算，我们的方法能够灵活组合罕见词处理能力，显著提升可扩展性与可复用性。跨多个领域的广泛实验表明，所提方法在目标词处理上达到或超越微调模型水平，将通用性能提升约5个BLEU值，并有效缓解了灾难性遗忘问题。