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Titlebook: Big Data and Artificial Intelligence; 11th International C Vikram Goyal,Naveen Kumar,Dhruv Kumar Conference proceedings 2023 The Editor(s)

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41#
發(fā)表于 2025-3-28 17:24:05 | 只看該作者
42#
發(fā)表于 2025-3-28 20:06:18 | 只看該作者
KG-CTG: Citation Generation Through Knowledge Graph-Guided Large Language Modelsset of standard S2ORC dataset, which only consists of computer science academic research papers in the English Language. Vicuna performs best for this task with 14.15 Meteor, 12.88 Rouge-1, 1.52 Rouge-2, and 10.94 Rouge-L. Also, Alpaca performs best, and improves the performance by 36.98% in Rouge-1
43#
發(fā)表于 2025-3-28 23:49:41 | 只看該作者
SciPhyRAG - Retrieval Augmentation to?Improve LLMs on?Physics Q &Ae and . increase on ROUGE-2 scores. This approach has the potential to be used to reshape Physics Q &A by LLMs and has a lasting impact on their use for Physics education. Furthermore, the data sets released can be a reference point for future research and educational domain tasks such as . and ..
44#
發(fā)表于 2025-3-29 04:17:20 | 只看該作者
45#
發(fā)表于 2025-3-29 10:02:48 | 只看該作者
GEC-DCL: Grammatical Error Correction Model with?Dynamic Context Learning for?Paragraphs and Scholar we substantiate the efficacy of our approach, achieving substantial F. score enhancements: 77% increase, 19.61% boost, and 10.49% rise respectively, compared to state-of-the-art models. Furthermore, we contrast our model’s performance with LLaMA’s GEC capabilities. We extend our investigation to sc
46#
發(fā)表于 2025-3-29 12:02:37 | 只看該作者
A Deep Learning Emotion Classification Framework for?Low Resource Languageslassification model is selected through experimentation that compares machine learning models and pre-trained models. Machine learning and deep learning models are SVM, Logistic Regression, Random Forest, CNN, BiLSTM, and CNN+BiLSTM. The pre-trained models, mBERT, IndicBERT, and a hybrid model, mBER
47#
發(fā)表于 2025-3-29 17:32:05 | 只看該作者
48#
發(fā)表于 2025-3-29 21:31:51 | 只看該作者
49#
發(fā)表于 2025-3-30 01:30:45 | 只看該作者
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發(fā)表于 2025-3-30 04:32:45 | 只看該作者
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