LLMs and Compound AI Systems - Exploring Potential Applications in Real-World Scenarios

Abstract

Compound AI Systems are the emerging phenomena integrating “large language models (LLMs)” with additional components like agents, retrievers, orchestrators, and tools to deal with issues of individual models in tasks which need reasoning, memory, multimodal knowledge, and real-time grounding. These systems compose various specialized modules into cohesive flows for more context-aware and capable behaviors. Irrespective of rising adoption in both industry and academia, the landscape of compound AI systems has been fragmented, with a lack of unified model for taxonomy, analysis, and evaluation. In LLMs, recent advancements and AI systems have made a significant change in optimization and design of complex workflows. Compound AI systems have been adept with various components when it comes to perform smart tasks. With the rise in complex systems, there are new challenges when it comes to optimizing both interactions and components. While traditional optimization models are foundational, such as “reinforcement learning (RL)” and “supervised fine-tuning (SFT)”, there are promising new avenues with the rise of “natural language processing (NLP)”, especially when it comes to optimizing different systems. This study offers a systematic review of recent developments for compound AI systems. It validates the notion of optimizing compound AI systems, highlights open research challenges, and classifies current approaches apart from major dimensions.

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