This research delves into the issue of hallucination detection in Large Language Models (LLMs) using Arabic language datasets. As LLMs are increasingly being used in various applications, the phenomenon of hallucination, which refers to generating factually inaccurate content despite grammatical coherence, poses significant challenges. We participate in the OSACT 2024 Shared-task (Detection of Hallucination in Arabic Factual Claims Generated by ChatGPT and GPT4). We explore various approaches for detecting and mitigating hallucination, using models such as GPT-4, Mistral, and Gemini within a novel experimental framework. Our research findings reveal that the effectiveness of these models in classifying claims into Fact-Claim, Fact-Improvement, and Non-Fact categories varies greatly, underscoring the complexities of addressing hallucination in morphologically rich languages. The study emphasizes the need for advanced modelling and training strategies to enhance the reliability and factual accuracy of LLM-generated content, laying the groundwork for future explorations in mitigating hallucination risks. In our experiments we achieved a 0.54 F1 in GPT-4 LLM.

Sarcasm detection and sentiment analysis are important tasks in Natural Language Understanding. Sarcasm is a type of expression where the sentiment polarity is flipped by an interfering factor. In this study, we exploited this relationship to enhance both tasks by proposing a multi-task learning approach using a combination of static and contextualised embeddings. Our proposed system achieved the best result in the sarcasm detection subtask.

Twitter and other social media platforms offer users the chance to share their ideas via short posts. While the easy exchange of ideas has value, these microblogs can be leveraged by people who want to share hatred. and such individuals can share negative views about an individual, race, or group with millions of people at the click of a button. There is thus an urgent need to establish a method that can automatically identify hate speech and offensive language. To contribute to this development, during the OSACT4 workshop, a shared task was undertaken to detect offensive language in Arabic. A key challenge was the uniqueness of the language used on social media, prompting the out-of-vocabulary (OOV) problem. In addition, the use of different dialects in Arabic exacerbates this problem. To deal with the issues associated with OOV, we generated a character-level embeddings model, which was trained on a massive data collected carefully. This level of embeddings can work effectively in resolving the problem of OOV words through its ability to learn the vectors of character n-grams or parts of words. The proposed systems were ranked 7th and 8th for Subtasks A and B, respectively.

Social media platforms such as Twitter offer people an opportunity to publish short posts in which they can share their opinions and perspectives. While these applications can be valuable, they can also be exploited to promote negative opinions, insults, and hatred against a person, race, or group. These opinions can be spread to millions of people at the click of a mouse. As such, there is a need to develop mechanisms by which offensive language can be automatically detected in social media channels and managed in a timely manner. To help achieve this goal, SemEval 2020 offered a shared task (OffensEval 2020) that involved the detection of offensive text in Arabic. We propose an ensemble approach that combines different levels of word embedding models and transfers learning from other sources of emotion-related tasks. The proposed system ranked 9th out of the 52 entries within the Arabic Offensive language identification subtask.