Tokenization Efficiency and Inference Latency in Romanized Nepali Across Llama-3.1, Mistral, and Qwen

This report synthesises findings from 3 peer-reviewed papers addressing the following research question: What is the correlation between tokenization efficiency and inference latency for Romanized Nepali tasks across Llama-3.1, Mistral, and Qwen architectures. Romanized Nepali, the Nepali language written in the Latin alphabet, is the dominant medium for informal digital communication in Nepal, yet it remains critically underresourced in the landscape of Large Language Models (LLMs). This study presents a systematic benchmarking of. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the correlation between tokenization efficiency and inference latency for Romanized Nepali tasks across Llama-3.1, Mistral, and Qwen architectures?

Autonomous literature synthesis. Automated review score: 7.7/10. Full text and citation available at Assignee Research.