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：关于通过微调、检索增强生成（Retrieval-Augmented Generation, RAG）以及软提示（Soft-Prompting）提升大语言模型（LLMs）性能的研究，往往侧重于采用高难度或高成本的技术手段，这使得许多新方法对非技术用户而言相对难以触及。本文测试了未经修改的GPT 3.5版本、经微调的版本，以及同一未修改模型在接入向量化RAG数据库时（分别独立使用及结合基础非算法软提示）的性能表现。我们测试了各模型回答一组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。结果表明，若使用商业平台并采用默认设置（无迭代以建立输出基线），微调模型的性能优于GPT 3.5 Turbo，而RAG方法则优于前两者。应用软提示显著提升了每种方法的性能。