Papers with cross-lingual generalisation
Are Pretrained Multilingual Models Equally Fair across Languages? (2022.coling-1)
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| Challenge: | Pretrained multilingual language models can help bridge the digital language divide, enabling high-quality NLP models for lower-resourced languages. |
| Approach: | They propose to use a multilingual dataset to examine whether multilingual models are equally fair across languages. |
| Outcome: | The proposed model enables apples-to-apples comparison across languages of group disparities in multilingual language models. |
Multilingual Reasoning via Self-training (2025.naacl-long)
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| Challenge: | Recent studies have introduced eclectic strategies to improve reasoning beyond English, but these methods are related to specific language that is not always optimal for reasoning. |
| Approach: | They propose a modular approach that instructs models to structure reasoning passages in a different problem space and then self-refines their capabilities to deliver step-wise reasoning passage. |
| Outcome: | The proposed approach achieves significant improvements in multilingual reasoning of various models and task, with improved reasoning consistency across languages. |
Turning English-centric LLMs Into Polyglots: How Much Multilinguality Is Needed? (2024.findings-emnlp)
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| Challenge: | Existing models that target a single language are not seen during finetuning, but are able to respond in multiple languages once deployed in downstream applications. |
| Approach: | They investigate the minimal amount of multilinguality required during finetuning to elicit effective cross-lingual generalisation in English-centric LLMs. |
| Outcome: | The proposed model can respond in as few as two to three languages to a user's query in English, but the degree to which a target language is seen during pretraining is limiting. |
Training Multi-Modal LLMs through Dialogue Planning for HRI (2025.findings-acl)
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| Challenge: | Existing approaches to enhance Multi-Modal Large Language Models (MLLMs) with explicit dialogue planning improves response accuracy and quality, and allows models trained in one language to transfer effectively to another. |
| Approach: | They propose an approach that enhances Multi-Modal Large Language Models with a novel explicit dialogue planning phase that allows agents to refine their understanding of ambiguous commands. |
| Outcome: | The proposed approach reduces hallucinations and improves task feasibility by fine-tuning and assessing Multi-Modal models in human-robot interaction scenarios. |