Search Results for author:
Found 4 papers, 2 papers with code
StablePT: Towards Stable Prompting for Few-shot Learning via Input Separation
Large language models have shown their ability to become effective few-shot learners with prompting, revoluting the paradigm of learning with data scarcity.
Does DetectGPT Fully Utilize Perturbation? Bridge Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better
Hence, we propose a novel fine-tuned detector, Pecola, bridging metric-based and fine-tuned detectors by contrastive learning on selective perturbation.
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot Learning
However, prior discrete prompt optimization methods require expert knowledge to design the base prompt set and identify high-quality prompts, which is costly, inefficient, and subjective.
CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive Learning
Machine-Generated Text (MGT) detection, a task that discriminates MGT from Human-Written Text (HWT), plays a crucial role in preventing misuse of text generative models, which excel in mimicking human writing style recently.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
