𝗗𝗮𝘁𝗮 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗳𝗼𝗿 𝗟𝗟𝗠𝘀

𝗗𝗮𝘁𝗮 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗳𝗼𝗿 𝗟𝗟𝗠𝘀

You want a better AI model. Many think more data solves every problem. This is a mistake.

Instruction data scaling has a limit. Quality beats quantity. A small set of clean examples works best. Low quality data hurts the model. It creates errors. Real world tests prove this.

Follow these rules:

• Prioritize data quality.
• Use diverse examples.
• Stop adding data when performance stops growing.

Clean data leads to better results.

Source: https://dev.to/paperium/exploring-the-impact-of-instruction-data-scaling-on-large-language-models-anempirical-study-on-2gl1 Optional learning community: https://t.me/GyaanSetuAi