Zero-shot POS tagging accuracy comparison of Gemini 2.0 Flash Thinking, Llama 3, and Claude 3 on XTREME for low-resource
Domaine:
natural language processingCréateur:
Ass
Éditeur:
Hôte:
Named Entity Recognition (NER) and Part-of-Speech (POS) tagging are critical tasks for Natural Language Processing (NLP), yet their availability for low-resource languages (LRLs) like Bodo remains limited. This article presents a comparative empirical study investigating the effectiveness of Google's Gemini 2.0 Flash Thinking Experiment model for zero-shot cross-lingual transfer of POS and NER tagging to Bodo. We explore two distinct methodologies: (1) direct translation of English sentences to Bodo followed by tag transfer, and (2) prompt-based tag transfer on parallel English-Bodo sentence p
Research goal: How does the zero-shot POS tagging accuracy of Gemini 2.0 Flash Thinking compare to Llama 3 and Claude 3 on the XTREME benchmark for low-resource Tibeto-Burman languages beyond Bodo?
Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 9.2/10.
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