Prompting and In-Context Learning: Optimizing Prompts for Mistral Large

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Higginbotham, Nathan S. Matthews This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4430993/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The exploration of the synergy between prompting and in-context learning reveals significant improvements in the performance of language models when tailored instructions and relevant context are integrated. The research delves into various prompt designs, assessing their impact on tasks such as text summarisation, machine translation, and question-answering. Prompts that include clear, explicit instructions and contextual information significantly enhance model outputs in terms of accuracy, coherence, and relevance. Experiments with the Mistral Large model demonstrate that adaptive prompting, which dynamically adjusts based on real-time interactions, can further refine model performance. Challenges such as balancing the amount of context to avoid information overload and the sensitivity of model responses to subtle changes in prompt phrasing are addressed. The study's findings underscore the critical role of effective prompt engineering and contextual integration in maximising the potential of language models. Future research directions include developing systematic methods for prompt design, optimising contextual information, and exploring cross-task generalisation. This research contributes valuable insights into guiding and informing language models, paving the way for more intelligent and adaptive AI systems across diverse applications. Artificial Intelligence and Machine Learning Prompt Engineering In-Context Learning Language Models Model Performance Adaptive Prompting Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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