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Financial Causal QA via Instruction and Prompt Tuning of Gemma3-12B

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.