few-shot-priming 0.0.552

Analyzing priming effects in a few shot setting environment

Analyzing Priming Effect in Prompt-based learning

How does priming affect prompt-based learing? This project aims at analyzing this effect in stance classification. We train a stance classifier on the ibm stance classification dataset by fine-tuning a GPT-2 model with a prompt and analzing how does the selection of the few shots used in the prompt affect the performance of the model. Our main assumption is that the examples chosen should be chosen in a diverse manner with regard topic.

1. To evaluate the prompt-fine-tuning, run the following command

• Hyperparamter optimization

python scripts/run_prompt_fine_tuning.py --validate --optimize 

• Best Hyperparameters

python scripts/run_prompt_fine_tuning.py --validate --optimize 

2. To evaluate the in-context (prompt) setup run

python scripts/run_prompt_fine_tuning.py --validate --optimize 

3. To evaluate DeBERTa (a normal classifier) with all hyperparameters, run the following

python scripts/optimize_baseline.py 

The results of the experiments will be logged to your home directory. The parameters can be saved in config.yaml