DFKI-NLP at SemEval-2024 Task 2: Towards Robust LLMs Using Data Perturbations and MinMax Training

Building on the methodology outlined (NLP) has seen significant advancements, beginning by Kanakarajan and Sankarasubbu (2023), with the introduction of word embeddings we assessed the zero-shot performance of various (Mikolov et al., 2013), followed by transformer instruction-tuned LLMs to identify the most effective architectures like BERT (Vaswani et al., 2017; Devlin model. Upon selecting the best LLM, we introduced et al., 2019), and specialized language models an auxiliary module during the fine-tuning (LMs) such as BioBERT (Lee et al., 2020) and process, which emphasized learning "hard" examples. PubMedBERT (Gu et al., 2021) tailored for the Taking inspiration from Korakakis and Vlachos biomedical domain. The advent of large language (2023), who experimented with various configurations models (LLMs) like GPT-3 (Brown et al., 2020), for the auxiliary module and highlighted commonly known as Chat-GPT, has further pushed its substantial impact on the final NLI system's the boundaries of NLP, showcasing capabilities performance, we explored various architectures for in diverse NLP tasks and even reasoning.