Fine-tuning GPT-2 from human preferences

1. 1Collect human labels for specific tasks like summarization and text continuation
2. 2Fine-tune a pre-trained language model like GPT-2 using the collected labels
3. 3Evaluate the performance of the fine-tuned model on the target tasks
4. 4Refine the model by adjusting hyperparameters or incorporating additional feedback

NLP researchers and AI engineers can benefit from this research to improve language model performance, while product managers can consider applications of fine-tuned models in real-world tasks

💡 Human feedback can be used to fine-tune language models and adapt them to specific tasks and preferences

🤖 Fine-tuning GPT-2 with human feedback improves performance on tasks like summarization!