Large language models (LLMs) have demonstrated impressive learning capabilities in a few steps, quickly adapting to new tasks with only a handful of examples.
However, despite their progress, LLMs still face limitations in complex reasoning involving chaotic contexts overloaded with disjoint facts. To address this challenge, researchers have explored techniques such as chain-of-thought prompting that guide models to progressively analyze information. Yet, on their own, these methods struggle to fully capture all critical details in large contexts.
This paper proposes a technique combining Thread-of-Thought (ToT) prompting with a retrieval augmented generation (RAG) framework accessing multiple knowledge graphs in parallel. While ToT acts as the “backbone” of reasoning that structures thinking, the RAG system expands the available knowledge to fill in the gaps. Parallel querying of various information sources improves efficiency and coverage compared to sequential retrieval. Together, this framework aims to improve the understanding and problem-solving abilities of LLMs in chaotic contexts, approximating human cognition.
We begin by emphasizing the need for structured reasoning in chaotic environments where relevant and irrelevant facts are mixed. Next, we present the design of the RAG system and how it expands the accessible knowledge of an LLM. We then explain the integration of ToT prompts to methodically guide the LLM through a step-by-step analysis. Finally, we discuss optimization strategies such as parallel retrieval to efficiently query multiple knowledge sources simultaneously.
Through both a conceptual explanation and Python code examples, this article highlights a new technique for orchestrating the strengths of an LLM with complementary external knowledge. Creative integrations like this highlight promising directions for overcoming inherent model limitations and advancing AI reasoning capabilities. The proposed approach aims to provide a generalizable framework that can be improved as LLMs and knowledge bases evolve.
