A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks
CX Liang, P Tian, CH Yin, Y Yua, W An-Hou… - arXiv preprint arXiv …, 2024 - arxiv.org
This survey and application guide to multimodal large language models (MLLMs) explores
the rapidly developing field of MLLMs, examining their architectures, applications, and …
the rapidly developing field of MLLMs, examining their architectures, applications, and …
A Survey on Data Synthesis and Augmentation for Large Language Models
K Wang, J Zhu, M Ren, Z Liu, S Li, Z Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
The success of Large Language Models (LLMs) is inherently linked to the availability of vast,
diverse, and high-quality data for training and evaluation. However, the growth rate of high …
diverse, and high-quality data for training and evaluation. However, the growth rate of high …
LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?
The generative large language models (LLMs) are increasingly being used for data
augmentation tasks, where text samples are LLM-paraphrased and then used for classifier …
augmentation tasks, where text samples are LLM-paraphrased and then used for classifier …
LLM-DER: A Named Entity Recognition Method Based on Large Language Models for Chinese Coal Chemical Domain
L Xiao, Y Xu, J Zhao - arXiv preprint arXiv:2409.10077, 2024 - arxiv.org
Domain-specific Named Entity Recognition (NER), whose goal is to recognize domain-
specific entities and their categories, provides an important support for constructing domain …
specific entities and their categories, provides an important support for constructing domain …
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy Interpolation
While fine-tuning of pre-trained language models generally helps to overcome the lack of
labelled training samples, it also displays model performance instability. This instability …
labelled training samples, it also displays model performance instability. This instability …
Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classification
The generative large language models (LLMs) are increasingly used for data augmentation
tasks, where text samples are paraphrased (or generated anew) and then used for classifier …
tasks, where text samples are paraphrased (or generated anew) and then used for classifier …
Exploring LLM-based Data Annotation Strategies for Medical Dialogue Preference Alignment
This research examines the use of Reinforcement Learning from AI Feedback (RLAIF)
techniques to improve healthcare dialogue models, with the aim of tackling the challenges of …
techniques to improve healthcare dialogue models, with the aim of tackling the challenges of …