Implicit Sentiment Analysis on Complicated Web Text

We propose AdaInfer, a lightweight algorithm that adaptively stops LLM inference early based on statistical cues, cutting up to 43% of computation with <1% performance loss and no model modification.

We propose Few-Shot Detector (FSD), a novel AI-generated image detector that learns a metric space to recognize unseen fake images with only a few samples, achieving +11.6% higher accuracy and strong generalization without retraining.

To lower the computional cost of training large model, we focus on speeding up pre-training by progressively growing from a small Transformer structure to a large one.

We approach comparative opinion classification through prompt learning, taking the advantage of embedded knowledge in pre-trained language model. We design a twin framework with dual prompts, named CORT. This extremely simple model delivers state-of-the-art and robust performance on all benchmark datasets for comparative opinion classification. We believe CORT well serves as a new baseline for comparative opinion classification.

We propose the Context and Former-Label Enhanced Net (CofeNet) for quotation extraction. CofeNet is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets and one proprietary dataset, we show that our achieves state-of-the-art performance on complicated quotation extraction.