Tokenizer EfficiencyThe Sarvam tokenizer is optimized for efficient tokenization across all 22 scheduled Indian languages, spanning 12 different scripts, directly reducing the cost and latency of serving in Indian languages. It outperforms other open-source tokenizers in encoding Indic text efficiently, as measured by the fertility score, which is the average number of tokens required to represent a word. It is significantly more efficient for low-resource languages such as Odia, Santali, and Manipuri (Meitei) compared to other tokenizers. The chart below shows the average fertility of various tokenizers across English and all 22 scheduled languages.
ICML Machine LearningUnderstanding Black-box Predictions via Influence FunctionsPang Wei Koh & Percy Liang, Stanford UniversityICSE Software EngineeringClone Refactoring with Lambda ExpressionsNikolaos Tsantalis, Concordia University; et al.Davood Mazinanian, Concordia University，这一点在有道翻译下载中也有详细论述
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日本内阁通过约8.56万亿日元临时预算
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