Papers with monolingual and cross-lingual settings
Towards Code-switched Classification Exploiting Constituent Language Resources (2020.aacl-srw)
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| Challenge: | Code-switching is a communicative phenomenon denoting a shift from one language to another within the same speech exchange. |
| Approach: | They propose to convert code-switched data into its constituent high resource languages for use in both monolingual and cross-lingual settings. |
| Outcome: | The proposed code-switching language can be used for multiple downstream tasks . the proposed language increases the F1 score by 22% and 42.5% compared to the state-of-the-art. |
GenKnowSub: Improving Modularity and Reusability of LLMs through General Knowledge Subtraction (2025.acl-short)
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| Challenge: | Large language models (LLMs) struggle with zero-shot generalization due to entanglement of general knowledge and task-specific adaptations. |
| Approach: | They propose a modular framework that disentangles general knowledge and adaptations by constructing a library of task-specific LoRA modules alongside a general-domain LoRA. |
| Outcome: | The proposed framework disentangles general knowledge and task-specific adaptations . it generates residual modules that focus more exclusively on task-relevant information . |
A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders (2021.emnlp-main)
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| Challenge: | Existing studies have used probing tasks to verify the presence of linguistic properties in vector representations, but it is unclear whether they can be manipulated to indirectly steer them. |
| Approach: | They validate a geometric mapping technique to transform linguistic properties without tuning . they use a pre-trained multilingual autoencoder to transform three linguistic property . |
| Outcome: | The proposed method can be used without tuning of the pre-trained autoencoder . the results are validated in monolingual and cross-lingual settings . |
Large Language Models for Multilingual Previously Fact-Checked Claim Detection (2025.findings-emnlp)
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| Challenge: | a new study evaluates large language models for multilingual previously fact-checked claim detection . authors assess seven LLMs across 20 languages in monolingual and cross-lingual settings . |
| Approach: | They evaluate large language models for multilingual previously fact-checked claim detection . they find they perform well for high-resource languages, struggle with low-resourced languages . |
| Outcome: | The proposed model performs well for high-resource languages, but struggle with low-resourced languages. |