Large language models (LLMs) demonstrate some reasoning abilities, though it's debated whether they truly reason or rely on information retrieval. Prompt engineering enhances reasoning, employing techniques like Chain-of-Thought (CoT), which involves intermediate reasoning steps. Multi-stage prompts, problem decomposition, and external tools are also used. Multi-agent discussions may not surpass a well-prompted single LLM. Research explores knowledge graphs and symbolic solvers to improve LLM reasoning, and methods to make LLMs more robust against irrelevant context. The field continues to investigate techniques to improve reasoning in LLMs.

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