SkipCLM: Enhancing Crosslingual Alignment of Decoder Transformer Models via Contrastive Learning and Skip Connection (2025.naacl-srw)
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| Challenge: | Existing contrastive learning methods for cross-lingual alignment are not effective for multilingual machine translation tasks. |
| Approach: | They propose a method that augments contrastive learning for cross-lingual alignment with a trainable skip connection to preserve information crucial for accurate target language generation. |
| Outcome: | Experiments with XGLM-564M on the Flores-101 benchmark show that the proposed method preserves crucial information crucial for accurate target language generation. |
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