Training with Ciphers to Enhance In-Context Terminology Learning For Translation from Rare Languages
Fine-tuning LLMs with in-context examples (IC-Train) lets translation models adapt to new parallel data at deployment, but it is unclear whether they actually learn the translations of rare terms from those examples. We formalize this capability as in-context term learning (ICTL). We show that standard IC-Train develops this capability only weakly, and propose RareCipherFT, which strengthens it without any lexical resources. RareCipherFT pairs each training target with in-context examples that share a rare term with it. It then replaces that term with a character-shuffled cipher in both the context and the reference, so the term can only be recovered by copying from context. The ciphered ...
| Authors | Deevyanshu Malu, Deeptanshu Malu, Sunita Sarawagi |
|---|---|
| Primary category | cs.CL |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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