I’ve been fine-tuning Llama-2 using LoRA. The task is to classify the title into correct categories/extract relevant named entities.
However, when I ran the inference, Llama-2 always produces very unstable spellings although it was trained on another spelling.
Example 1:
Input: L’Occitane Premium Hand Cream
Training data:
Beauty products | Hand Cream
Llama-2 output:
Beauty products | Hand Créme
Example 2:
Input: J&J Talcum Powder
Training data:
Brand: J&J
Llama-2 output:
Brand: Johnson & Johnson
Example 3:
Input: Bluetooth Keyboard for Microsoft Surface 2022
Training data:
Electronics | Tablet | Accessories
Llama-2 output:
Electronics | Tablets | Accessaries
Do you know how to force Llama-2 to stay faithful to the input texts’ spellings?