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.

Most large language models (LLMs) are trainedon massive datasets that include private infor-mation, which may be disclosed to third-partyusers in output generation. Developers put de-fences to prevent the generation of harmful andprivate information, but jailbreaking methodscan be used to bypass them. Machine unlearn-ing aims to remove information that may beprivate or harmful from the model’s genera-tion without retraining the model from scratch.While machine unlearning has gained somepopularity to counter the removal of privateinformation, especially in English, little to noattention has been given to Turkish unlearn-ing paradigms or existing benchmarks. In thisstudy, we introduce TUNE (Turkish Unlearn-ing Evaluation), the first benchmark datasetfor Turkish unlearning task for personal infor-mation. TUNE consists of 9842 input-targettext pairs about 50 fictitious personalities withtwo training task types: (1) Q A and (2) In-formation Request. We fine-tuned the mT5base model to evaluate various unlearning meth-ods, including our proposed approach. We findthat while current methods can help unlearnunwanted private information in Turkish, theyalso unlearn other information we want to re-tain in the model.