19 citations · 70 across the 20 of their papers we have counts for
11 papers · 1 filter
Optimizing Large Language Models for Turkish: New Methodologies in Corpus Selection and Training
H. Toprak Kesgin, M. Kaan Yuce, Eren Dogan +7
In this study, we develop and assess new corpus selection and training methodologies to improve the effectiveness of Turkish language models. Specifically, we adapted Large Languag…
Cosmos-LLaVA: Chatting with the Visual Cosmos-LLaVA: Görselle Sohbet Etmek
Ahmed Zeer, Eren Dogan, Yusuf Erdem +7
In this study, a Turkish visual instruction model was developed and various model architectures and dataset combinations were analysed to improve the performance of this model. The…
Scaling BERT Models for Turkish Automatic Punctuation and Capitalization Correction
Abdulkader Saoud, Mahmut Alomeyr, Himmet Toprak Kesgin +1
This paper investigates the effectiveness of BERT based models for automated punctuation and capitalization corrections in Turkish texts across five distinct model sizes. The model…
LegalTurk Optimized BERT for Multi-Label Text Classification and NER
Farnaz Zeidi, Mehmet Fatih Amasyali, Çiğdem Erol
The introduction of the Transformer neural network, along with techniques like self-supervised pre-training and transfer learning, has paved the way for advanced models like BERT.…
Introducing cosmosGPT: Monolingual Training for Turkish Language Models
H. Toprak Kesgin, M. Kaan Yuce, Eren Dogan +5
The number of open source language models that can produce Turkish is increasing day by day, as in other languages. In order to create the basic versions of such models, the traini…
Türkçe Dil Modellerinin Performans Karşılaştırması Performance Comparison of Turkish Language Models
Eren Dogan, M. Egemen Uzun, Atahan Uz +6
The developments that language models have provided in fulfilling almost all kinds of tasks have attracted the attention of not only researchers but also the society and have enabl…
Theoretical research on generative diffusion models: an overview
Melike Nur Yeğin, Mehmet Fatih Amasyalı
Generative diffusion models showed high success in many fields with a powerful theoretical background. They convert the data distribution to noise and remove the noise back to obta…
Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving
Gulsum Yigit, Mehmet Fatih Amasyali
Math Word Problem (MWP) solving presents a challenging task in Natural Language Processing (NLP). This study aims to provide MWP solvers with a more diverse training set, ultimatel…
Advancing NLP Models with Strategic Text Augmentation: A Comprehensive Study of Augmentation Methods and Curriculum Strategies
Himmet Toprak Kesgin, Mehmet Fatih Amasyali
This study conducts a thorough evaluation of text augmentation techniques across a variety of datasets and natural language processing (NLP) tasks to address the lack of reliable,…
Investigating Semi-Supervised Learning Algorithms in Text Datasets
Himmet Toprak Kesgin, Mehmet Fatih Amasyali
Using large training datasets enhances the generalization capabilities of neural networks. Semi-supervised learning (SSL) is useful when there are few labeled data and a lot of unl…
Iterative Mask Filling: An Effective Text Augmentation Method Using Masked Language Modeling
Himmet Toprak Kesgin, Mehmet Fatih Amasyali
Data augmentation is an effective technique for improving the performance of machine learning models. However, it has not been explored as extensively in natural language processin…