Sentiment Analysis is a crucial task in Natural Language Processing (NLP) focused on identifying and categorizing emotional tones or opinions within text. For Arabic customer reviews, sentiment analysis is particularly challenging. The language’s rich diversity, with numerous regional dialects differing significantly from Modern Standard Arabic (MSA) and each other in lexicon, syntax, and sentiment expression, complicates consistent performance across dialects. In this paper, we present our approach, submitted to the AHASIS Shared Task 2025, focusing on sentiment analysis for Arabic dialects in the hotel domain. Our method leverages the capabilities of GPT-4o through adaptive few-shot prompting technique, where similar contextual examples are dynamically selected for each review using a k-Nearest Neighbors (kNN) search over train embeddings from a fine-tuned encoder model. This approach tailors the prompt to each specific instance, enhancing classification performance over minority class. Our submission achieved an F1-score of 76.0% on the official test set, showing stronger performance for the Saudi dialect compared to Darija.

Rhetorical Role Labeling (RRL) of legal judgments is essential for various tasks, such as case summarization, semantic search and argument mining. However, it presents challenges such as inferring sentence roles from context, interrelated roles, limited annotated data, and label imbalance. This study introduces novel techniques to enhance RRL performance by leveraging knowledge from semantically similar instances (neighbours). We explore inference-based and training-based approaches, achieving remarkable improvements in challenging macro-F1 scores. For inference-based methods, we explore interpolation techniques that bolster label predictions without re-training. While in training-based methods, we integrate prototypical learning with our novel discourse-aware contrastive method that work directly on embedding spaces. Additionally, we assess the cross-domain applicability of our methods, demonstrating their effectiveness in transferring knowledge across diverse legal domains.