Research into methods for improving the performance of large language models (LLMs) through fine-tuning, retrieval-augmented generation (RAG) and soft-prompting has tended to focus on the use of highly technical or high-cost techniques, making many of the newly discovered approaches comparatively inaccessible to non-technical users. In this paper we tested an unmodified version of GPT 3.5, a fine-tuned version, and the same unmodified model when given access to a vectorised RAG database, both in isolation and in combination with a basic, non-algorithmic soft prompt. In each case we tested the model's ability to answer a set of 100 questions relating primarily to events that occurred after September 2021 (the point at which GPT 3.5's training data set ends). We found that if commercial platforms are used and default settings are applied with no iteration in order to establish a baseline set of outputs, a fine-tuned model outperforms GPT 3.5 Turbo, while the RAG approach out-performed both. The application of a soft prompt significantly improved the performance of each approach.

翻译：针对提升大语言模型性能的方法（如微调、检索增强生成及软提示）研究，长期聚焦于高度技术化或高成本的实现手段，导致许多新方法对非技术用户而言难以普及。本研究对未修改版GPT 3.5、微调版模型以及可访问向量化RAG数据库的同一未修改模型进行了测试，既单独测试了后者的表现，也测试了其与基础非算法软提示的组合效果。在每种场景下，我们测试了模型回答100个问题的能力，这些问题主要涉及2021年9月（GPT 3.5训练数据集截止时间）之后发生的事件。研究发现，若采用商业平台并应用默认设置（不进行迭代以建立基线输出结果），微调模型表现优于GPT 3.5 Turbo，而RAG方法则优于前两者。此外，软提示的应用显著提升了各类方法的性能。