Fine-Tuning Effects on Pass@1 Accuracy for Romanized Nepali in Llama-3.1-8B, Mistral-7B, and Qwen3-8B

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: What is the impact of fine-tuning on the pass@1 accuracy of Llama-3.1-8B, Mistral-7B-v0.1, and Qwen3-8B for Romanized Nepali language tasks using the same bilingual dataset. 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. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of fine-tuning on the pass@1 accuracy of Llama-3.1-8B, Mistral-7B-v0.1, and Qwen3-8B for Romanized Nepali language tasks using the same bilingual dataset?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance