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.

翻译：大型语言模型（LLM）的出现彻底改变了机器学习及相关领域，展现出在理解、生成和处理人类语言方面的卓越能力。然而，通过基于API的文本提示提交的传统使用方式在上下文约束和外部资源可用性方面存在一定局限。为解决这些挑战，我们提出了一种名为强化检索增强机器学习（RRAML）的新型框架。RRAML将LLM的推理能力与专门构建的检索器从用户提供的大规模数据库中获取的支持信息相结合。通过利用强化学习的最新进展，我们的方法有效解决了若干关键挑战。首先，它规避了访问LLM梯度的需求。其次，我们的方法减轻了为特定任务重新训练LLM的负担，因为由于模型访问受限和计算强度高，这通常不切实际或不可能实现。此外，我们将检索器的任务与推理器无缝连接，从而减少幻觉并降低不相关且可能有害的检索文档的影响。我们相信，本文提出的研究议程有潜力深刻影响人工智能领域，使广泛实体能够民主化地访问和利用LLM。