Instruction Tuning Data Quality vs. Quantity in Low-Resource Romanized Scripts for 7B--8B LLMs

This report synthesises findings from 13 peer-reviewed papers addressing the following research question: How does instruction tuning data quality versus quantity affect pass@1 accuracy for low-resource Romanized scripts in 7B-8B parameter LLMs. Rapid developments in large language models (LLMs) have created new opportunities for their use in the energy sector, from forecasting renewable energy to power system operation and energy market analysis. These models help improve decision-making, anomaly detection, and. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does instruction tuning data quality versus quantity affect pass@1 accuracy for low-resource Romanized scripts in 7B-8B parameter LLMs?

Autonomous literature synthesis. Automated review score: 8.8/10. Full text and citation available at Assignee Research.