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We introduce two novel methods, Tree-Search and Self-contextualizing QA,designed to enhance the performance of large language models (LLMs) inquestion-answering tasks. Tree-Search is a sampling technique specificallycreated to extract diverse information from an LLM for a given prompt.Self-contextualizing QA leverages Tree-Search to enable the model to create itsown context using a wide range of information relevant to the prompt, evaluateit explicitly and return a open book answer to the initial prompt . Wedemonstrate that the quality of generated answers improves according to variousmetrics, including accuracy, informativeness, coherence, and consistency, asevaluated by GPT3.5(text-davinci-003). Furthermore, we show that our methodsresult in increased robustness and that performance is positively correlatedwith tree size, benefiting both answer quality and robustness. Finally, wediscuss other promising applications of Tree-Search, highlighting its potentialto enhance a broad range of tasks beyond question-answering.  \noindent We also discuss several areas for future work, including refiningthe Tree-Search and Self-Contextualizing QA methods, improving the coherence ofthe generated context, and investigating the impact of bootstrapping on modelrobustness

Writing your own book: A method for going from closed to open book QA to improve robustness and performance of smaller LLMs