Morocco

Large Language Models (LLMs) achieve strong performance on many tasks, but they still struggle with morphologically rich, low-resource languages such as Turkish. This difficulty stems from Turkish being an agglutinative language and underrepresented in multilingual training data, which causes current models to often fail at capturing its morphology, flexible word order, and formal registers. In this paper, we introduce MODA (Model Adapted for Domain Applications), a Turkish-specialized LLM built via a modular pipeline that combines continual pre-training, parameter-efficient fine-tuning, and model merging. Starting from Qwen2.5-7B as the base model, we first perform large-scale continual pre-training on a Turkish web corpus to improve grammatical and morphological representations. We then apply parameter-efficient supervised fine-tuning on task-oriented instruction data, and finally merge specialized variants into a single production-ready model. We evaluate MODA on TurkishMMLU, the Turkish subset of EXAMS, and TRCLAIM-19, where it consistently outperforms both the base and instruction-tuned Qwen2.5-7B models. Our results support a training strategy that explicitly separates linguistic acquisition from task alignment when adapting LLMs to morphologically rich, underrepresented languages under realistic hardware constraints.

EACL 2026

Building a Turkish Large Language Model via Continual Pre-Training and Parameter-Efficient

lora adaptation

turkish llm

continual pre-training

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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

Text-to-SQL systems have achieved strong performance on English benchmarks, yet their behavior in morphologically rich, low-resource languages remains largely unexplored. We introduce BIRDTurk, the first Turkish adaptation of the BIRD benchmark, constructed through a controlled translation pipeline that adapts schema identifiers to Turkish while strictly preserving the logical structure and execution semantics of SQL queries and databases. Translation quality is validated using a CLT-based statistical framework, achieving 98.15% accuracy on human-evaluated samples. Using BIRDTurk, we evaluate inference-based prompting, agentic multi-stage reasoning, and supervised fine-tuning. Our results reveal that Turkish introduces consistent performance degradation—driven by both structural linguistic divergence and underrepresentation in LLM pretraining—while agentic reasoning demonstrates stronger cross-lingual robustness. Supervised fine-tuning remains challenging for standard multilingual baselines but scales effectively with modern instruction-tuned models. BIRDTurk provides a controlled testbed for cross-lingual Text-to-SQL evaluation under realistic database conditions. We release the training and development splits to support future research.

BIRDTurk: A Large-Scale Adaptation of the Text-to-SQL Dataset to Turkish

Most large language models (LLMs) are trained on massive datasets that include private infor- mation, which may be disclosed to third-party users in output generation. Developers put de- fences to prevent the generation of harmful and private information, but jailbreaking methods can be used to bypass them. Machine unlearn- ing aims to remove information that may be private or harmful from the model’s genera- tion without retraining the model from scratch. While machine unlearning has gained some popularity to counter the removal of private information, especially in English, little to no attention has been given to Turkish unlearn- ing paradigms or existing benchmarks. In this study, we introduce TUNE (Turkish Unlearn- ing Evaluation), the first benchmark dataset for Turkish unlearning task for personal infor- mation. TUNE consists of 9842 input-target text pairs about 50 fictitious personalities with two training task types: (1) Q&A and (2) In- formation Request. We fine-tuned the mT5 base model to evaluate various unlearning meth- ods, including our proposed approach. We find that while current methods can help unlearn unwanted private information in Turkish, they also unlearn other information we want to re- tain in the model.

TUNE: A Task For Turkish Machine Unlearning For Data Privacy

With the recent surge in the development of large language models, the need for comprehensive and language-specific evaluation benchmarks has become critical. While significant progress has been made in evaluating English-language models, benchmarks for other languages, particularly those with unique linguistic characteristics such as Turkish, remain less developed. Our study introduces TurkBench, a comprehensive benchmark designed to assess the capabilities of generative large language models in the Turkish language. TurkBench involves 8,151 data samples across 21 distinct subtasks. These are organized under six main categories of evaluation: Knowledge, Language Understanding, Reasoning, Content Moderation, Turkish Grammar and Vocabulary, and Instruction Following. The diverse range of tasks and the culturally relevant data would provide researchers and developers with a valuable tool for evaluating their models and identifying areas for improvement. We further publish our benchmark for online submissions at https://huggingface.co/turkbench

TurkBench: A Benchmark for Evaluating Turkish Large Language Models

In this paper, we investigate how transformer models represent complex verb paradigms in Turkish and Modern Hebrew, focusing on how tokenization strategies shape this ability. Using the Blackbird Language Matrices task on natural data, we show that for Turkish—with its transparent morphological markers—both monolingual and multilingual models succeed either when tokenization is highly atomic or breaking words into small subword units. For Hebrew, however, a multilingual model using character-level tokenization fails to capture its non-concatenative morphology, while a monolingual model with unified morpheme-aware segmentation excels. Performance improves on more synthetic datasets, in all models.

Modelling the Morphology of Verbal Paradigms: A Case Study in the Tokenization of Turkish and Hebrew

Current interpretability methods for Large Language Models (LLMs) operate on a fundamental yet flawed assumption: that subword tokens represent independent semantic units. We prove that this assumption creates a fidelity bottleneck in Morphologically Rich Languages (MRLs), where semantic meaning is densely encoded in sub-token morphemes. We term this phenomenon the Tokenization-Morphology Misalignment (TMM). To resolve TMM, we introduce MAFEX (Morpheme-Aligned Faithful Explanations), a theoretically grounded framework that redefines feature attribution as a linear projection from the computational (token) basis to the linguistic (morpheme) basis. We evaluate our method on a diverse suite of Turkish LLMs, including BERTurk, BERTurk-Sentiment, Cosmos-BERT, and Kumru-2B. On our embedded benchmark (N=20), MAFEX achieves an average F1@1 of 91.25% compared to 13.75% for standard token-level baselines (IG, SHAP, DeepLIFT), representing a +77.5% absolute improvement, establishing it as the new standard for faithful multilingual interpretability.

Beyond the Token: Correcting the Tokenization Bias in XAI via Morphologically-Aligned Projection

This study presents a framework for generating the gold-standard summary fully automatically and reproducibly based on multiple human summaries of Turkish educational videos. Within the scope of the study, a new dataset called TR-EduVSum was created, encompassing 82 Turkish course videos in the field of "Data Structures and Algorithms" and containing a total of 3281 independent human summaries. Inspired by existing pyramid-based evaluation approaches, the AutoMUP (Automatic Meaning Unit Pyramid) method is proposed, which extracts consensus-based content from multiple human summaries. AutoMUP clusters the meaning units extracted from human summaries using embedding, statistically models inter-participant agreement, and generates graded summaries based on consensus weight. In this framework, the gold summary corresponds to the highest-consensus AutoMUP configuration, constructed from the most frequently supported meaning units across human summaries. Experimental results show that AutoMUP summaries exhibit high semantic overlap with robust LLM summaries such as Flash 2.5 and GPT-5.1. Furthermore, ablation studies clearly demonstrate the decisive role of consensus weight and clustering in determining summary quality. The proposed approach can be generalized to other Turkic languages at low cost.

TR-EduVSum: A Turkish-Focused Dataset and Consensus Framework for Educational Video Summarization

Sarcasm is a colloquial form of language that is used to convey messages in a non-literal way, which affects the performance of many NLP tasks. Sarcasm detection is not trivial and existing work mainly focus on only English. We present SarcasTürk, a context-aware Turkish sarcasm detection dataset built from Ekşi Sözlük entries, a large-scale Turkish online discussion platform where people frequently use sarcasm. SarcasTürk contains 1,515 entries from 98 titles with binary sarcasm labels and a title-level context field created to support comparisons between entry-only and context-aware models. We generate these contexts by selecting representative sentences from all entries under a title using summarization techniques. We report baseline results for a fine-tuned BERTurk classifier and zero-shot LLMs under both no-context and context-aware conditions. We find that BERTurk model with title-level context has the best performance with 0.76 accuracy and balanced class-wise F1 scores (0.77 for sarcasm, 0.75 for no sarcasm). SarcasTürk can be shared upon contacting the authors since the dataset contains potentially sensitive and offensive language.

SarcasTürk: Turkish Context-Aware Sarcasm Detection Dataset

RAGTurk: Best Practices for Retrieval Augmented Generation in Turkish

Minimal pair benchmarks have become a common approach for evaluating the syntactic knowledge of language models (LMs). However, the creation of such benchmarks often overlooks language-specific confounders that may affect model performance, particularly in the case of morphologically rich languages. In this paper, we investigate how surface-level factors such as morpheme count, subword count, and sentence length influence the performance of LMs on a Turkish benchmark of linguistic minimal pairs. We further analyze whether a tokenizer’s degree of alignment with morphological boundaries can serve as a proxy for model performance. Finally, we test whether the distribution of morphemes in a minimal pair benchmark can skew model performance. Our results show that while surface factors have limited predictive power, they might still serve as a systematic source of bias. Moreover, we find that morphological alignment can roughly correspond to model performance, and morpheme-level imbalances in the benchmark may have a significant influence on results.

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