  Open-source LLMs have shown great potential as fine-tuned chatbots, and
demonstrate robust abilities in reasoning and surpass many existing benchmarks.
Retrieval-Augmented Generation (RAG) is a technique for improving the
performance of LLMs on tasks that the models weren't explicitly trained on, by
leveraging external knowledge databases. Numerous studies have demonstrated the
effectiveness of RAG to more successfully accomplish downstream tasks when
using vector datasets that consist of relevant background information. It has
been implicitly assumed by those in the field that if adversarial background
information is utilized in this context, that the success of using a RAG-based
approach would be nonexistent or even negatively impact the results. To address
this assumption, we tested several open-source LLMs on the ability of RAG to
improve their success in answering multiple-choice questions (MCQ) in the
medical subspecialty field of Nephrology. Unlike previous studies, we examined
the effect of RAG in utilizing both relevant and adversarial background
databases. We set up several open-source LLMs, including Llama 3, Phi-3,
Mixtral 8x7b, Zephyr$\beta$, and Gemma 7B Instruct, in a zero-shot RAG
pipeline. As adversarial sources of information, text from the Bible and a
Random Words generated database were used for comparison. Our data show that
most of the open-source LLMs improve their multiple-choice test-taking success
as expected when incorporating relevant information vector databases.
Surprisingly however, adversarial Bible text significantly improved the success
of many LLMs and even random word text improved test taking ability of some of
the models. In summary, our results demonstrate for the first time the
countertintuitive ability of adversarial information datasets to improve the
RAG-based LLM success.
