Papers by David Guzmán
AlignFreeze: Navigating the Impact of Realignment on the Layers of Multilingual Models Across Diverse Languages (2025.naacl-short)
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| Challenge: | Realignment techniques are often employed to enhance cross-lingual transfer in multilingual language models, but can degrade performance in languages that differ significantly from the fine-tuned source language. |
| Approach: | They propose a method that freezes either the lower half or upper half of the layers during realignment to prevent performance degradation. |
| Outcome: | The proposed method improves Part-of-Speech (PoS) tagging performance in languages where realignment fails. |
Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation (2024.findings-naacl)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) methods are important in low-resource language (LRL) Neural Machine Translation (NMT) but their practical effectiveness varies significantly across different languages. |
| Approach: | They evaluated the performance of 8 parameters-efficient fine-tuning methods with 15 architectures using the SacreBLEU score. |
| Outcome: | The Houlsby+Inversion adapter outperforms the baseline architectures in both in-domain and out-domain tests and the Houlson+Inverter achieves the best performance overall. |
MERLIN: Multi-Stage Curriculum Alignment for Multilingual Encoder-LLM Integration in Cross-Lingual Reasoning (2026.eacl-long)
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Kosei Uemura, David Guzmán, Quang Phuoc Nguyen, Jesujoba Oluwadara Alabi, En-Shiun Annie Lee, David Ifeoluwa Adelani
| Challenge: | Existing methods to align large language models with multilingual encoders raise accuracy for low-resource languages (LRLs) but performance of LLMs in low- and high-resourced languages remains a problem. |
| Approach: | They propose a model-stacking framework that iteratively refines in 2-stages based on a curriculum strategy and adapts only a small set of DoRA weights. |
| Outcome: | The proposed framework improves exact-match accuracy by +12.9 pp over MindMerger and outperforms GPT-4o-mini by 15.2 pp on the AfriMGSM benchmark. |