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. |
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| Challenge: | despite lack of explicit cross-lingual training data, multilingual models can achieve cross-linguistic transfer. |
| Approach: | They find alignment is significantly correlated with cross-lingual transfer . they advocate for further research on realignment methods for smaller models . |
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Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
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| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
| Approach: | They propose a taxonomy of methods to improve cross-lingual alignment . they argue that an effective trade-off between language-neutral and language-specific information is key . |
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Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMs (2025.acl-long)
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| Challenge: | Effective cross-lingual transfer is hindered by performance gaps and the scarcity of fine-tuning data in many languages. |
| Approach: | They propose a middle-layer alignment objective integrated into task-specific training to improve cross-lingual transfer across languages. |
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Can you map it to English? The Role of Cross-Lingual Alignment in the Multilingual Performance of LLMs (2026.eacl-long)
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| Challenge: | Large language models (LLMs) can answer prompts in many languages despite being pre-trained mostly on English text. |
| Approach: | They propose a Discriminative Alignment Index to quantify instance-level alignment across 24 languages other than English and three distinct NLU tasks. |
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AlignX: Advancing Multilingual Large Language Models with Multilingual Representation Alignment (2025.emnlp-main)
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| Challenge: | Multilingual large language models (LLMs) possess impressive multilingual understanding and generation capabilities, but performance and cross-lingual alignment often lag for non-dominant languages. |
| Approach: | They propose a representation-level framework to enhance multilingual performance of pre-trained LLMs by integrating multilingual semantic alignment and language feature integration. |
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Unveiling Linguistic Regions in Large Language Models (2024.acl-long)
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| Challenge: | Existing studies on how LLMs achieve cross-lingual alignment and generalization have not explored the intrinsic mechanisms of how they achieve crosslingual alignment. |
| Approach: | They propose to remove a core region that corresponds to linguistic competence and set parameters to zero to reduce performance across 30 different languages. |
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Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer (2026.acl-srw)
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| Challenge: | Large language models (LLMs) have advanced natural language processing, yet their benefits remain concentrated in English and a small number of high-resource languages. |
| Approach: | They fine-tuned large language models (4B–671B parameters) on Arabic and evaluated zero-shot reading comprehension on Semitic languages and non-Semitic controls. |
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The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training (2026.findings-acl)
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Rui Zhang, Hongwei Li, Yun Shen, Xinyue Shen, Wenbo Jiang, Guowen Xu, Yang Liu, Michael Backes, Yang Zhang
| Challenge: | Misaligned large language models can magnify harm by exploiting them to undermine safety . et al., 2022b; Bai e.t., 2023): misalignment, realignment and model-specific resistance are important . |
| Approach: | They evaluate four methods to identify a mechanism asymmetry between attack and defense . they find that ORPO is most effective for misalignment, but DPO excels in realignment . |
| Outcome: | The proposed methods show a mechanism asymmetry between attack and defense . the proposed methods excel in realignment, but at the expense of model utility . |
Cool-Fusion: Fuse Large Language Models without Training (2025.acl-long)
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| Challenge: | Cool-Fusion is a simple yet effective approach to combine two or more heterogeneous large language models . |
| Approach: | They propose a method that fuses the knowledge of two or more heterogeneous large language models to leverage complementary strengths. |
| Outcome: | The proposed method increases accuracy from three strong source LLMs on GSM8K by 17.4%. |
LayAlign: Enhancing Multilingual Reasoning in Large Language Models via Layer-Wise Adaptive Fusion and Alignment Strategy (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) are pretrained on multilingual corpora but exhibit suboptimal performance on low-resource languages. |
| Approach: | They propose a framework that integrates representations from all encoder layers and an adaptive fusion-enhanced attention mechanism to enable layer-wise interaction between the LLM and the multilingual encoder. |
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