Chengyu Dong

SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to Rank

2023

Deep neural classifiers trained with cross-entropy loss (CE loss) often suffer from poor calibration, necessitating the task of out-of-distribution (OOD) detection. Traditional supervised OOD detection methods require expensive manual annotation of in-distribution and OOD samples. …

Evaluating the Smooth Control of Attribute Intensity in Text Generation with LLMs

2024 · arXiv (Cornell University)

Controlling the attribute intensity of text generation is crucial across scenarios (e.g., writing conciseness, chatting emotion, and explanation clarity). The remarkable capabilities of large language models (LLMs) have revolutionized text generation, prompting us to explore …