chain-of-thought ↔ LLM-based agent

Relations (1)

related 2.00 — strongly supporting 3 facts

Chain-of-thought is a reasoning technique used by LLM-based agents to decompose complex queries into manageable steps [1], which improves their cognitive performance [2] and allows them to systematically query external tools or knowledge bases [3].

Facts (3)

Sources

How to Improve Multi-Hop Reasoning With Knowledge Graphs and ... neo4j.com Neo4j 2 facts

procedureAn LLM agent using a chain-of-thought flow to answer a question about the founders of Prosper Robotics follows this procedure: (1) separates the query into sub-questions ('Who is the founder of Prosper Robotics?' and 'What’s the latest news about the founder?'), (2) queries a knowledge graph to identify the founder as Shariq Hashme, and (3) rewrites the second question to 'What’s the latest news about Shariq Hashme?' to retrieve the final answer.

claimLLM agents utilize chain-of-thought flows to separate complex questions into multiple steps, define a plan, and query tools such as APIs or knowledge bases to generate answers.

The Synergy of Symbolic and Connectionist AI in LLM ... arxiv.org arXiv 1 fact

claimThe Chain-of-Thought (CoT) method enhances the cognitive task performance of LLM-empowered agents by guiding the models to generate text about intermediate reasoning steps.