9 citations · 16 across the 12 of their papers we have counts for
13 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…
Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification
Yuxuan Hu, Chenwei Zhang, Min Yang +3
With the rapid development of deep learning methods, there have been many breakthroughs in the field of text classification. Models developed for this task have been shown to achie…
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…
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…
APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation
Yuxuan Hu, Minghuan Tan, Chenwei Zhang +5
Empathetic response generation is designed to comprehend the emotions of others and select the most appropriate strategies to assist them in resolving emotional challenges. Empathy…
CLHA: A Simple yet Effective Contrastive Learning Framework for Human Alignment
Feiteng Fang, Liang Zhu, Min Yang +6
Reinforcement learning from human feedback (RLHF) is a crucial technique in aligning large language models (LLMs) with human preferences, ensuring these LLMs behave in beneficial a…
MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property
Shiwen Ni, Minghuan Tan, Yuelin Bai +9
Large language models (LLMs) have demonstrated impressive performance in various natural language processing (NLP) tasks. However, there is limited understanding of how well LLMs p…
Layer-wise Regularized Dropout for Neural Language Models
Shiwen Ni, Min Yang, Ruifeng Xu +2
Among the various pre-trained neural language models that are popular today, dropout is already an indispensable regularization technique. To solve the inconsistency between traini…
History, Development, and Principles of Large Language Models-An Introductory Survey
Zichong Wang, Zhibo Chu, Thang Viet Doan +3
Language models serve as a cornerstone in natural language processing (NLP), utilizing mathematical methods to generalize language laws and knowledge for prediction and generation.…
E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models
Jinchang Hou, Chang Ao, Haihong Wu +8
With the accelerating development of Large Language Models (LLMs), many LLMs are beginning to be used in the Chinese K-12 education domain. The integration of LLMs and education is…
Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models
Shiwen Ni, Dingwei Chen, Chengming Li +3
Recent advancements in Large Language Models (LLMs) have showcased their remarkable capabilities in text understanding and generation. However, even stronger LLMs are susceptible t…