Large language models (LLMs) have impressive few-shot learning capabilities, but they still struggle with complex reasoning in chaotic contexts. This article proposes a technique that combines Thread-of-Thought (ToT) prompting with a Retrieval Augmented Generation (RAG) framework to enhance LLMs’ understanding and problem-solving abilities. The RAG system accesses multiple knowledge graphs in parallel, improving efficiency and coverage. By integrating structured reasoning and external knowledge, LLMs can approach human-like cognition.
Achieving Structured Reasoning with LLMs in Chaotic Contexts with Thread of Thought Prompting and Parallel Knowledge Graph Retrieval
Introduction
Large language models (LLMs) have shown impressive few-shot learning capabilities, quickly adapting to new tasks with just a few examples. However, LLMs still face limitations in complex reasoning involving chaotic contexts overloaded with disjoint facts. To address this challenge, researchers have proposed a technique that combines Thread-of-Thought (ToT) prompting with a Retrieval Augmented Generation (RAG) framework. This framework aims to enhance LLMs’ understanding and problem-solving abilities in chaotic contexts, moving closer to human cognition.
The Need for Structured Reasoning
LLMs have made strides in language understanding, but their reasoning abilities remain limited. In chaotic contexts, relevant and irrelevant facts intermix, making it difficult for models to determine what information is important. The lack of structure in these contexts makes it challenging for models to methodically build an understanding grounded in the relevant information. This article proposes the use of ToT prompting, inspired by human cognition, to guide models through step-by-step analysis of chaotic contexts.
Introducing the RAG System
The RAG system combines the capabilities of LLMs with scalable retrieval. It uses multiple knowledge graphs (KGs) indexed using the LLama index to broaden the available knowledge for LLMs. By querying diverse KGs in parallel, the RAG system improves efficiency and coverage compared to sequential retrieval. This system enables LLMs to adapt rapidly using few-shot learning instead of encoding all information statically.
Integrating ToT Prompting
To enable structured reasoning, ToT prompting is integrated into the RAG system. The super agent prompts the LLM to walk through the context in manageable parts step-by-step, summarizing and analyzing as it goes. This incremental analysis of retrieved passages enhances the model’s understanding and avoids overlooking critical facts.
Implementing Parallel Retrieval
To efficiently query multiple KGs, asynchronous parallel retrieval is used. This accelerates retrieval compared to sequential queries and expands the knowledge scope available to the model. Different graph algorithms and embeddings used in the KGs provide complementary reasoning strengths, allowing the model to employ the appropriate reasoning style for each query.
Query Ontology First
To provide context and establish basic concepts, the ontology is queried first before retrieving from the knowledge graphs. This “warm start” primes the RAG system with initial background information, allowing the knowledge graphs to fill in more specific relational facts tailored to the query.
Conclusion
The proposed approach combines ToT prompting with the RAG system to enhance LLMs’ reasoning abilities in complex contexts. By integrating structured sequencing of thought and parallel querying of diverse knowledge graphs, this approach aims to orchestrate the strengths of LLMs with complementary external knowledge sources. As LLMs and knowledge bases evolve, this technique provides a pathway to incrementally add structured reasoning capabilities. By creatively combining existing methods, we can achieve emergent capabilities greater than the sum of parts. With sustained research, the dream of human-like language understanding may gradually become a reality.
