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“We fine-tune XLM-RoBERTa on the WALS dataset to predict word order typology. Our model achieves 89.4% accuracy, on the WALS 2023 benchmark, outperforming previous BERT-based methods by 5%.”

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The fact that even the best LLMs score only 36% on WALS-Bench shows we are still in the early days of teaching machines to truly understand linguistic rules. However, by leveraging the structural data of WALS with the robustness of RoBERTa and the efficiency of Top-k attention, we are building the scaffolding for AI that doesn't just parrot text, but genuinely parses the architecture of human language. “We fine-tune XLM-RoBERTa on the WALS dataset to

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"That's not natural bedrock," Leo noted, kneeling. He took a crowbar and pried at the slate. It shifted, revealing a hollow sound beneath. Thud. Thud. The final interpretation returns to the home, connecting

(the cross-lingual version of RoBERTa), it allows for sophisticated analysis of how linguistic features influence model performance across different languages. Key Performance Highlights Cross-lingual Transfer Learning with Persian - SIGTYP