Bryndza at ClimateActivism 2024: Stance, Target and Hate Event Detection
  via Retrieval-Augmented GPT-4 and LLaMA
Marek Šuppa Daniel Skala Daniela Jašš Samuel Sučík Andrej Švec Peter Hraška
This study details our approach for the CASE 2024 Shared Task on Climate
Activism Stance and Hate Event Detection, focusing on Hate Speech Detection,
Hate Speech Target Identification, and Stance Detection as classification
challenges. We explored the capability of Large Language Models (LLMs),
particularly GPT-4, in zero- or few-shot settings enhanced by retrieval
augmentation and re-ranking for Tweet classification. Our goal was to determine
if LLMs could match or surpass traditional methods in this context.
  We conducted an ablation study with LLaMA for comparison, and our results
indicate that our models significantly outperformed the baselines, securing
second place in the Target Detection task. The code for our submission is
available at https://github.com/NaiveNeuron/bryndza-case-2024