Morocco

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&amp;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.

EACL 2026

TUNE: A Task For Turkish Machine Unlearning For Data Privacy

machine unlearning

turkish nlp

large language models

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community."<br>

*Message from the General Chair, Dirk Hovy*





<br>

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

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.

workshop paper

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)*<br><br>

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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. 

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.

A Morphology-Aware Evaluation of Turkish Syntax in Large Language Models

OCRTurk: A Comprehensive OCR Benchmark for Turkish

Idiomatic expressions are culturally grounded, semantically opaque, and difficult to interpret for multilingual natural language processing systems. Despite the large speaker population of Turkic languages, resources that focus on monolingual and cross-lingual idioms and their meanings are limited. We introduce the first unified benchmark for idiom understanding across Turkish, Azerbaijani, Turkmen, Gagauz, and Uzbek languages. The datasets compiled include token-level idiom span annotations. We develop models for idiom identification and semantic retrieval tasks. We evaluate seven models for idiom identification and nine embedding models for semantic retrieval tasks under several fine-tuning schemes using standard dense retrieval metrics. This benchmark provides a basis for studying idiomatic phenomena in Turkic languages and clarifies how idiomatic meanings are shared, altered, or diverge across languages.

A Unified Turkic Idiom Understanding Benchmark: Idiom Detection and Semantic Retrieval Across Five Turkic Languages

We present, to our knowledge, the first systematic transformer-based outlet-ideology classification study for Turkish news. Using a topic-balanced corpus of Turkish political articles drawn from six outlets commonly perceived as left-, centre-, or right-leaning, we formulate a three-way outlet-ideology classification task. On this dataset, we evaluate a monolingual encoder (BERTurk), two multilingual encoders (mBERT, XLM-R), and a LoRA-adapted decoder model (Mistral). BERTurk achieves the best performance among individual models (70% accuracy, 71% macro-F1), reaching levels comparable to English-language studies despite operating in a lower-resource setting. Error analyses show that all encoders reliably distinguish centrist from partisan articles, but frequently confuse left- and right-leaning articles with each other. Moreover, BERTurk is relatively stronger on right-leaning content, whereas the multilingual models favour left-leaning content, suggesting an “ideological fingerprint” of their pre-training data. Crucially, models fine-tuned on an English political-bias task fail to transfer to Turkish, collapsing to near-chance performance. Taken together, these results demonstrate that effective political bias detection requires target-language supervision and cannot be achieved through naïve cross-lingual transfer. Our work establishes a first baseline for Turkish political bias detection and underscores the need for open, carefully designed Turkish (and broader Turkic) bias benchmarks to support robust and fair media analysis.

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