# Rare Word Recognition and Translation Without Fine-Tuning via Task Vector in Speech Models

arXiv: https://arxiv.org/abs/2512.21894 · HTML: https://mlsanity.com/paper/2512.21894/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2025.

## Built on by (most cited first, 1 of 1)

- [Merging the Knowledge of LLMs for Automatic Speech Recognition](https://mlsanity.com/paper/2609.15743/) (2026)

## Abstract (arXiv)

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.