Titlebook: Natural Language Processing and Chinese Computing; 13th National CCF Co Derek F. Wong,Zhongyu Wei,Muyun Yang Conference proceedings 2025 Th

使委屈

書目名稱Natural Language Processing and Chinese Computing影響因子(影響力)




書目名稱Natural Language Processing and Chinese Computing影響因子(影響力)學(xué)科排名




書目名稱Natural Language Processing and Chinese Computing網(wǎng)絡(luò)公開度




書目名稱Natural Language Processing and Chinese Computing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Natural Language Processing and Chinese Computing被引頻次




書目名稱Natural Language Processing and Chinese Computing被引頻次學(xué)科排名




書目名稱Natural Language Processing and Chinese Computing年度引用




書目名稱Natural Language Processing and Chinese Computing年度引用學(xué)科排名




書目名稱Natural Language Processing and Chinese Computing讀者反饋




書目名稱Natural Language Processing and Chinese Computing讀者反饋學(xué)科排名

生命層

muffler

LasQ: Largest Singular Components Fine-Tuning for?LLMs with?Quantizationge generation tasks. The experiments show that our method can significantly outperform existing methods with fewer training parameters. Compared with LoftQ and QLoRA methods, it has a 2%–15% improvement, and it can even achieve equivalent LoRA fine-tuning effects and full parameter fine-tuning effects.

Cpr951

Sparse Mixture of?Experts Language Models Excel in?Knowledge Distillationillation using MoE without the necessity of continued pretraining. Experimental results indicate that our approach enhances the model’s capabilities compared to dense model distillation, achieving superior performance across a multitude of tasks. We will release our code at ..

隱語

Evaluation and?Analysis of?the?Chinese Semantic Dependency Understanding Ability of?Large Language Mderstanding of high-order semantic structure knowledge and semantic relation knowledge. Furthermore, our experiments reveal that while LLMs perform well on the in-domain (ID) test set via SFT, their generalization ability on out-of-domain (OOD) test set remains inadequate.

枯萎將要

SUGAR

0302-9743 ing and Chinese Computing, NLPCC 2024, held in Hangzhou, China, during November 2024..The 161 full papers and 33 evaluation workshop papers included in these proceedings were carefully reviewed and selected from 451 submissions. They deal with the following areas: Fundamentals of NLP; Information Ex

CALL

Improving Causal Inference of?Large Language Models with?SCM Toolsools, and combine the inference results of the causal inference tools to generate the final causal question answers. The experimental results show that the method proposed in this paper outperforms the best existing methods.

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