面向互联网复杂语境的文本隐式情感分析研究

我们提出了 AdaInfer，一种基于统计特征自适应提前终止 LLM 推理的轻量算法，可在不修改模型的情况下减少 最高 43% 的计算量，性能下降不足 1%。

我们提出了 Few-Shot Detector (FSD)，一种通过学习度量空间、仅需少量样本即可识别未见伪造图像的检测器，能在无需再训练的情况下实现 准确率提升 11.6% 并保持强泛化能力。

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.