We introduce two novel methods, Tree-Search and Self-contextualizing QA,
designed to enhance the performance of large language models (LLMs) in
question-answering tasks. Tree-Search is a sampling technique specifically
created to extract diverse information from an LLM for a given prompt.
Self-contextualizing QA leverages Tree-Search to enable the model to create its
own context using a wide range of information relevant to the prompt, evaluate
it explicitly and return a open book answer to the initial prompt . We
demonstrate that the quality of generated answers improves according to various
metrics, including accuracy, informativeness, coherence, and consistency, as
evaluated by GPT3.5(text-davinci-003). Furthermore, we show that our methods
result in increased robustness and that performance is positively correlated
with tree size, benefiting both answer quality and robustness. Finally, we
discuss other promising applications of Tree-Search, highlighting its potential
to enhance a broad range of tasks beyond question-answering.
\noindent We also discuss several areas for future work, including refining
the Tree-Search and Self-Contextualizing QA methods, improving the coherence of
the generated context, and investigating the impact of bootstrapping on model
robustness

本文介绍了两种新颖的方法：Tree-Search 和 Self-contextualizing QA，旨在提高大型语言模型在问答任务中的性能。通过使用 Tree-Search 采样技术和自我上下文化 QA 方法，可使模型根据提示创建其自己的上下文并生成高质量、一致性强、信息量大、连贯度强的答案，优点是具有更高的鲁棒性，同时可以扩展到多种任务和应用。