5 papers
A Survey on Large Language Model Benchmarks
Shiwen Ni, Guhong Chen, Shuaimin Li +11
In recent years, with the rapid development of the depth and breadth of large language models' capabilities, various corresponding evaluation benchmarks have been emerging in incre…
MGHFT: Multi-Granularity Hierarchical Fusion Transformer for Cross-Modal Sticker Emotion Recognition
Jian Chen, Yuxuan Hu, Haifeng Lu +4
Although pre-trained visual models with text have demonstrated strong capabilities in visual feature extraction, sticker emotion understanding remains challenging due to its relian…
FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature Drift
Yong Zhang, Feng Liang, Guanghu Yuan +3
Federated learning (FL) enables collaborative training of a global model in the centralized server with data from multiple parties while preserving privacy. However, data heterogen…
Lower Layers Matter: Alleviating Hallucination via Multi-Layer Fusion Contrastive Decoding with Truthfulness Refocused
Dingwei Chen, Feiteng Fang, Shiwen Ni +6
Large Language Models (LLMs) have demonstrated exceptional performance across various natural language processing tasks. However, they occasionally generate inaccurate and counterf…
Training on the Benchmark Is Not All You Need
Shiwen Ni, Xiangtao Kong, Chengming Li +4
The success of Large Language Models (LLMs) relies heavily on the huge amount of pre-training data learned in the pre-training phase. The opacity of the pre-training process and th…