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IndicSentEval: How Effectively do Multilingual Transformer Models encode Linguistic Properties for Indic Languages?

arXiv — cs.LG•Tuesday, November 4, 2025 at 5:00:00 AM
NeutralArtificial Intelligence

IndicSentEval: How Effectively do Multilingual Transformer Models encode Linguistic Properties for Indic Languages?

The article titled "IndicSentEval: How Effectively do Multilingual Transformer Models encode Linguistic Properties for Indic Languages?" explores the capabilities of multilingual transformer models in representing linguistic features specific to Indic languages. It situates this investigation within the broader context of recent advancements in natural language processing, emphasizing the progress made in model development. A key focus of the study is assessing the robustness of these models, particularly their reliability when processing variations in input text. By examining how effectively these models encode linguistic properties, the article contributes to understanding their practical applicability for Indic languages. This evaluation is crucial given the linguistic diversity and complexity inherent in Indic language families. Overall, the article provides insights into the current state of multilingual transformer models and their potential limitations in handling nuanced linguistic variations.

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