# T\\"urk\\c{c}e Dil Modellerinin Performans Kar\\c{s}{\\i}la\\c{s}t{\\i}rmas{\\i} Performance Comparison of Turkish Language Models Source: [T\\"urk\\c{c}e Dil Modellerinin Performans Kar\\c{s}{\\i}la\\c{s}t{\\i}rmas{\\i} Performance Comparison of Turkish Language Models]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:06.262Z Authors: Eren Dogan, M. Egemen Uzun, Atahan Uz, H. Emre Seyrek, Ahmed Zeer, Ezgi Sevi, H. Toprak Kesgin, M. Kaan Yuce, M. Fatih Amasyali Published: 2024-04-25T20:10:14.000Z ## Abstract 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 enabled them to become products. There are commercially successful language models available. However, users may prefer open-source language models due to cost, data privacy, or regulations. Yet, despite the increasing number of these models, there is no comprehensive comparison of their performance for Turkish. This study aims to fill this gap in the literature. A comparison is made among seven selected language models based on their contextual learning and question-answering abilities. Turkish datasets for contextual learning and question-answering were prepared, and both automatic and human evaluations were conducted. The results show that for question-answering, continuing pretraining before fine-tuning with instructional datasets is more successful in adapting multilingual models to Turkish and that in-context learning performances do not much related to question-answering performances. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()