Which of the following prompt engineering techniques is most effective for improving an LLM's performance on multi-step reasoning tasks?

Chain-of-thought (CoT) prompting is a technique specifically designed to improve the reasoning abilities of Large Language Models (LLMs) on complex, multi-step tasks. By providing the model with examples that include explicit, intermediate reasoning steps, CoT guides the model to break down a problem into a sequence of logical thoughts before arriving at a final answer. This process mimics human-like reasoning and has been shown to significantly enhance performance on arithmetic, commonsense, and symbolic reasoning problems that require multiple inferential steps.

A. Retrieval-augmented generation without context: Retrieval-augmented generation (RAG) is inherently dependent on retrieving and using external context to inform the model's response. RAG without context is a contradiction and would not function as intended.

B. Few-shot prompting with unrelated examples: The effectiveness of few-shot prompting relies on providing relevant examples that demonstrate the desired task format and logic. Unrelated examples would confuse the model and degrade its performance.

C. Zero-shot prompting with detailed task descriptions: While a detailed description is helpful, zero-shot prompting is often insufficient for complex reasoning tasks, as it does not explicitly show the model how to perform the intermediate reasoning steps.

1. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv preprint arXiv:2201.11903. (Section 2, "Chain-of-Thought Prompting," describes how providing a series of intermediate reasoning steps significantly improves performance on multi-step arithmetic and symbolic reasoning tasks). https://doi.org/10.48550/arXiv.2201.11903

2. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv preprint arXiv:2005.11401. (Section 2.1, "The RAG model," explains that the core mechanism involves a retriever finding relevant documents to be used as context). https://doi.org/10.48550/arXiv.2005.11401

3. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language Models are Few-Shot Learners. arXiv preprint arXiv:2005.14165. (Section 2.2, "Few-shot," demonstrates that performance scales with the number of relevant examples provided in the prompt's context). https://doi.org/10.48550/arXiv.2005.14165

4. Zhao, Z., et al. (2023). A Survey of Large Language Models. arXiv preprint arXiv:2303.18223. (Section 4.2.1, "Chain-of-Thought (CoT) Prompting," explicitly states that CoT is a key technique for enhancing the reasoning capabilities of LLMs by generating intermediate steps). https://doi.org/10.48550/arXiv.2303.18223