In this paper we present a novel methodology that harnesses the power of prompt tuning applied directly to Gemma3-12B, a state-of-the-art generative large language model to enhance performance on complex natural language processing challenges. Instead of relying solely on extensive retraining, our approach leverages carefully crafted input prompts to steer the pre-trained Gemma-12B towards generating outputs with superior contextual accuracy and interpretability. Our experimental evaluation employed a composite LLM Score metric that quantifies both semantic coherence and relevance; under this framework, our system (Team Name: Sarang) achieved a score of 4.54, ranking 9th in the shared task. Furthermore, in the competitive task evaluation, our method demonstrated the potential of prompt tuning as a viable alternative to traditional fine-tuning approaches. This study not only demonstrates the practical benefits of integrating prompt engineering with large language models but also opens avenues for future research aimed at further optimizing model performance in domain-specific applications.

This paper describes our approach for the FinCausal 2025 English Shared Task, aimed at detecting and extracting causal relationships from the financial text. The task involved answering context-driven questions to identify causes or effects within specified text segments. Our method utilized a consciousAI RoBERTa-base encoder model, fine-tuned on the SQuADx dataset. We further fine-tuned it using the FinCausal 2025 development set. To enhance the quality and contextual relevance of the answers, we passed outputs from the extractive model through Gemma2-9B, a generative large language model, for answer refinement. This hybrid approach effectively addressed the task’s requirements, showcasing the strength of combining extractive and generative models. We (Team name: Sarang) achieved outstanding results, securing 3rd rank with a Semantic Answer Similarity (SAS) score of 96.74% and an Exact Match (EM) score of 70.14%.