Papers by Enes Özeren
HYPEROFA: Expanding LLM Vocabulary to New Languages via Hypernetwork-Based Embedding Initialization (2025.acl-srw)
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| Challenge: | Pre-trained language models exhibit suboptimal performance on mid- and low-resource languages due to limited exposure to these languages during pre-training. |
| Approach: | They propose a similarity-based subword embedding initialization heuristic that introduces new tokens specific to target languages, initializes their embedders, and applies continual pre-training on target-language data. |
| Outcome: | The proposed method outperforms random initialization baseline and matches or exceeds OFA in both continual pre-training convergence and downstream task performance. |