The emergence of large language models (LLMs) has revolutionized machine
learning and related fields, showcasing remarkable abilities in comprehending,
generating, and manipulating human language. However, their conventional usage
through API-based text prompt submissions imposes certain limitations in terms
of context constraints and external source availability. To address these
challenges, we propose a novel framework called Reinforced Retrieval Augmented
Machine Learning (RRAML). RRAML integrates the reasoning capabilities of LLMs
with supporting information retrieved by a purpose-built retriever from a vast
user-provided database. By leveraging recent advancements in reinforcement
learning, our method effectively addresses several critical challenges.
Firstly, it circumvents the need for accessing LLM gradients. Secondly, our
method alleviates the burden of retraining LLMs for specific tasks, as it is
often impractical or impossible due to restricted access to the model and the
computational intensity involved. Additionally we seamlessly link the
retriever's task with the reasoner, mitigating hallucinations and reducing
irrelevant, and potentially damaging retrieved documents. We believe that the
research agenda outlined in this paper has the potential to profoundly impact
the field of AI, democratizing access to and utilization of LLMs for a wide
range of entities.

通过提出一种名为增强检索增强机器学习（RRAML）的新型框架，将大型语言模型的推理能力与用户提供的数据库中的检索信息相结合，有效地解决了 API 文本输入的上下文限制和外部数据源可用性的局限性。