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Found 10 papers, 4 papers with code
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
To bridge this gap, we introduce COIG-CQIA, a high-quality Chinese instruction tuning dataset.
MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property
In this paper, we contribute a new benchmark, the first Multilingual-oriented quiZ on Intellectual Property (MoZIP), for the evaluation of LLMs in the IP domain.
Layer-wise Regularized Dropout for Neural Language Models
To solve the inconsistency between training and inference caused by the randomness of dropout, some studies use consistency training to regularize dropout at the output layer.
History, Development, and Principles of Large Language Models-An Introductory Survey
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
The integration of LLMs and education is getting closer and closer, however, there is currently no benchmark for evaluating LLMs that focuses on the Chinese K-12 education domain.
Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models
In this paper, we propose a new paradigm for fine-tuning called F-Learning (Forgetting before Learning), which employs parametric arithmetic to facilitate the forgetting of old knowledge and learning of new knowledge.
ELECTRA is a Zero-Shot Learner, Too
Numerically, compared to MLM-RoBERTa-large and MLM-BERT-large, our RTD-ELECTRA-large has an average of about 8. 4% and 13. 7% improvement on all 15 tasks.
True or False: Does the Deep Learning Model Learn to Detect Rumors?
It is difficult for humans to distinguish the true and false of rumors, but current deep learning models can surpass humans and achieve excellent accuracy on many rumor datasets.
HAT4RD: Hierarchical Adversarial Training for Rumor Detection on Social Media
As such, the robustness and generalization of the current rumor detection model are put into question.
DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks
We compare the proposed method with other adversarial training methods and regularization methods, and our method achieves state-of-the-art on all datasets.
