Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Citation - Scopus: 19Thquad: Turkish Historic Question Answering Dataset for Reading Comprehension(Institute of Electrical and Electronics Engineers Inc., 2021) Soygazi,F.; Çiftçi,O.; Kök,U.; Cengiz,S.Question answering(QA) is a field in natural language processing and information retrieval, it aims to give answers to the questions using natural language. In this paper, we present the Turkish question answering dataset, which is THQuAD and baseline results with contextualized word embeddings. THQuAD consists of two different datasets one of them is TQuad on Turkish Islamic Science history within the scope of Teknofest 2018 "Artificial Intelligence competition", the second dataset on Ottoman history within the scope of Teknofest 2020 "Dogal Dil íçleme Yarismasi" prepared by us. THQuAD is a reading comprehension dataset, consisting of questions, answers, and passages. Our objective is to give an answer to a specific question by understanding the passage and extracting the answer from this passage. We generate contextualized word embeddings from pre-trained Turkish Bert, Electra, Albert language models after fine-tuning on different hyperparameters with neural networks. © 2021 IEEEConference Object Citation - Scopus: 11An Analysis of Large Language Models and Langchain in Mathematics Education(Association for Computing Machinery, 2023) Soygazi,F.; Oğuz, DamlaThe development of large language models (LLMs) has led to the consideration of new approaches, particularly in education. Word problems, especially in subjects like mathematics, and the need to solve these problems by collectively addressing specific stages of reasoning, have raised the question of whether LLMs can be successful in this area as well. In our study, we conducted analyses by asking mathematics questions especially related to word problems using ChatGPT, which is based on the latest language models like Generative Pretrained Transformer (GPT). Additionally, we compared the correct and incorrect answers by posing the same questions to LLMMathChain, a mathematics-specific LLM based on the latest language models like LangChain. It was observed that the answers obtained were more successful with ChatGPT (GPT 3.5), particularly in the field of mathematics. However, both language models were found to be below expectations, particularly in word problems, and suggestions for improvement were provided. © 2023 ACM.