prompt-based-learning

This paper investigates prompt-based learning for automatically generating highlights of academic papers, using models like GPT-2, T5, and ChatGPT, and shows that ChatGPT with few-shot prompts achieves performance comparable to or better than supervised methods without requiring task-specific training data.

This paper investigates few-shot biomedical relation extraction using prompt-based learning with LLMs, comparing pairwise classification and joint generation approaches. The best model achieves micro-F1 of 0.44, outperforming previous few-shot results but remaining below supervised baselines, while macro-F1 surpasses the supervised baseline on rare relation types.