Key Points
1. The paper introduces a new prompting approach called analogical prompting, which aims to automatically guide the reasoning process of large language models (LLMs) without the need for labeled exemplars.
2. The proposed method prompts LLMs to self-generate relevant exemplars or knowledge in the context before solving a given problem, drawing inspiration from analogical reasoning in humans.
3. Experimental results demonstrate that the analogical prompting approach outperforms 0-shot chain-of-thought (CoT) and manual few-shot CoT in various reasoning tasks, including mathematical problem solving, code generation, and other reasoning tasks.
4. The advancement and proficiency of large language models (LLMs) in various natural language processing (NLP) tasks, and the use of input prompts to guide LLMs in generating desired responses, mark the advent of the prompting era.
5. The proposed analogical prompting method addresses the challenges faced by existing 0-shot CoT and few-shot CoT methods by providing tailored exemplars for individual problems without the need for labeled data.
6. The experiments show that the analogical prompting approach, particularly in complex tasks like code generation, offers advantages such as detailed exemplars of reasoning without manual labeling and tailored exemplars for individual problems.
7. The results reveal that the method outperforms baselines across diverse reasoning-intensive tasks and base LLMs, achieving an average accuracy gain of +4%, with particular effectiveness in tasks involving diverse types of reasoning such as algebra, probability, and code generation.
8. The paper also discusses the comparison of self-generated exemplars versus retrieved exemplars, stating that self-generation offers convenience and versatility, while retrieval offers reliability. Empirical results show that self-generation performs better with larger-scale LLMs.
9. The paper concludes by highlighting the limitations of the proposed approach, such as increased inference computation compared to other prompting methods, dependence on the strength of the LLM, and sensitivity to specific prompt phrases
Summary
The research paper introduces a new prompting approach called analogical prompting to guide the reasoning process of Large Language Models (LLMs). This method is proposed as a response to the challenges faced by the existing chain-of-thought (CoT) prompting paradigm. The paper discusses the inspiration for analogical prompting from analogical reasoning in psychology and explains the strategy for prompting LLMs to self-generate relevant exemplars and high-level knowledge to effectively address new problems.
The authors compare their generation-based CoT with retrieval-based CoT and discuss its outperformance, especially with larger base LLMs. Experimental results show that the proposed approach outperforms 0-shot CoT and manual few-shot CoT in various reasoning tasks, including math problem solving in GSM8K and MATH, code generation in Codeforces, and other reasoning tasks in BIG-Bench. The paper also presents a qualitative analysis of the model outputs, discusses performance gains achieved by generating knowledge alongside exemplars, and presents a comparison of the proposed method with other prompting approaches. It also discusses the limitations of the proposed approach, such as increased inference computation and the reliance on the strength of the LLMs.
Overall, the approach is shown to offer detailed, customized exemplars for individual problems without requiring labeled data, effectively addressing the challenges faced by existing prompting methods.
Reference: https://arxiv.org/abs/2310.01714