Unlearning is a critical capability for ensuring privacy, security, and compliance in AI systems, enabling models to forget specific data while retaining overall performance. In this work, we participated in Task 4 of SemEval 2025, which focused on unlearning across three sub-tasks: (1) long-form synthetic creative documents, (2) short-form synthetic biographies containing personally identifiable information, and (3) real documents sampled from the target model’s training dataset. We conducted four experiments, employing Supervised Fine-Tuning (SFT) and Negative Preference Optimization (NPO). Despite achieving good performance on the retain set—data that the model was supposed to remember—our findings demonstrate that these techniques did not perform well on the forget set, where unlearning was required.

This paper explores the efficacy of large language models (LLMs) for Persian. While ChatGPT and consequent LLMs have shown remarkable performance in English, their efficiency for more low-resource languages remains an open question. We present the first comprehensive benchmarking study of LLMs across diverse Persian language tasks. Our primary focus is on GPT-3.5-turbo, but we also include GPT-4 and OpenChat-3.5 to provide a more holistic evaluation. Our assessment encompasses a diverse set of tasks categorized into classic, reasoning, and knowledge-based domains. To enable a thorough comparison, we evaluate LLMs against existing task-specific fine-tuned models. Given the limited availability of Persian datasets for reasoning tasks, we introduce two new benchmarks: one based on elementary school math questions and another derived from the entrance exams for 7th and 10th grades. Our findings reveal that while LLMs, especially GPT-4, excel in tasks requiring reasoning abilities and a broad understanding of general knowledge, they often lag behind smaller pretrained models fine-tuned specifically for particular tasks. Additionally, we observe improved performance when test sets are translated to English before inputting them into GPT-3.5. These results highlight the significant potential for enhancing LLM performance in the Persian language. This is particularly noteworthy due to the unique attributes of Persian, including its distinct alphabet and writing styles. We have made our codes, prompts, and data available here: https://github.com/Ipouyall/Benchmarking_ChatGPT_for_Persian.