In this work, we address the detection of drug abuse repercussions in Reddit posts, as part of SMM4H-HeaRD Task 7: Extraction of Social and Clinical Impacts of Substance Use from Social Media Posts. We evaluate multiple approaches, including fine-tuning and zero-shot inference, across several deep learning architectures. Our best result is obtained using an adapter-based fine-tuning approach on the DeBERTaV3 model. In addition, we explore text-based evolutionary optimization for Gemma 4 workflows and show that, on this task, they achieve competitive performance with the supervised DeBERTaV3 setup.

This paper presents the systems we developed for ArGuard 2026 Task B, addressing Arabic prompt safety through binary safe/unsafe classification (B1) and fine-grained assignment of unsafe prompts to harm domains (B2). Our central strategy is cross-lingual data augmentation: we machine-translate a large English prompt-safety corpus into Arabic and reuse it across both subtasks. For B1 we fine-tune the Arabic-native ALLaM-7B on self-generated rationales using the granular labels from B2 as signal. We compare against label-only training; our submission ranks 2nd (macro F₁=0.924). The experiments show the augmentation drives accuracy and not the self-generated chain-of-thought. For B2 we fine-tune Arabic encoders (full and adapter tuning) with a two-stage "warm’ pipeline that first trains the encoder on the easier binary task, before specialising it to the seven domains; our submission ranks 5th (macro F₁=0.762)

The paper details our approach to SemEval 2025 Shared Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval.We investigate how large language models (LLMs) designed for general-purpose retrieval via text-embeddings can be adapted for fact-checked claim retrieval across multiple languages, including scenarios where the query and fact-check are in different languages. The experiments involve fine-tuning with a contrastive objective, resulting in notable gains in both accuracy and efficiency over the baseline retrieval model. We evaluate cost-effective techniques such as LoRA and QLoRA and Prompt Tuning.Additionally, we demonstrate the benefits of Matryoshka embeddings in minimizing the memory footprint of stored embeddings, reducing the system requirements for a fact-checking system.