Abstract
Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks, but typically needs labeled exemplars of the reasoning process. In this work, we introduce a new prompting approach, analogical prompting, designed to automatically guide the reasoning process of LLMs. Inspired by analogical reasoning, a cognitive process in which humans draw from relevant past experiences to tackle new problems, our approach prompts language models to self-generate relevant exemplars or knowledge in the context, before proceeding to solve the given problem. This method presents several advantages: it obviates the need for labeling or retrieving exemplars, offering generality and convenience; it can also tailor the generated exemplars and knowledge to each problem, offering adaptability. Experimental results show that our approach outperforms 0-shot CoT and manual few-shot CoT in a variety of reasoning tasks, including math problem solving in GSM8K and MATH, code generation in Codeforces, and other reasoning tasks in BIG-Bench.
Overview
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This paper introduces analogical prompting as a novel technique to enhance reasoning in LLMs by enabling autonomous generation of relevant exemplars, which is inspired by human analogical reasoning.
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Analogical prompting eliminates the need for manual labeling and tailors problem-specific exemplars, improving over traditional Chain-of-Thought (CoT) methods such as 0-shot CoT and few-shot CoT.
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The method is validated across multiple datasets, including GSM8K, MATH, Codeforces, and BIG-Bench, showing consistent performance improvements over existing CoT strategies, with broad practical and theoretical implications.
LLMs as Analogical Reasoners
The paper entitled "LLMs as Analogical Reasoners," authored by Michihiro Yasunaga, Xinyun Chen, Yujia Li, Panupong Pasupat, Jure Leskovec, Percy Liang, Ed H. Chi, and Denny Zhou, introduces a novel approach to enhancing reasoning capabilities in LLMs via analogical prompting. This method advances over existing Chain-of-Thought (CoT) strategies by enabling models to autonomously generate relevant exemplars, drawing inspiration from human analogical reasoning processes.
Introduction
Recent advancements in LLMs have underscored their proficiency in handling complex tasks when guided effectively. Traditional CoT approaches such as 0-shot CoT and few-shot CoT have showcased the importance of intermediate reasoning steps in improving task performance. However, these methods have limitations: 0-shot CoT often provides overly generic instructions, and few-shot CoT requires labeled exemplars, which are costly to produce.
Main Contribution
The central contribution of this paper is the introduction of analogical prompting—a method that prompts LLMs to self-generate relevant exemplars before solving a problem. This framework is inspired by the cognitive process of analogical reasoning, where humans utilize past experiences to address new problems. The core advantages of this approach include:
- Automation Without Labeling: The method eliminates the need for manual labeling or retrieval of exemplars, thus enhancing generality and ease of use.
- Problem-Specific Tailoring: LLMs generate exemplars and knowledge tailored to each specific problem, thereby offering more nuanced guidance compared to static exemplars used in traditional few-shot CoT.
Methodology
Analogical prompting is operationalized through two main techniques:
- Self-Generated Exemplars: Given a problem, LLMs are instructed to generate relevant problems and their solutions based on prior knowledge. This is followed by solving the initial problem using the context of these generated exemplars.
- Self-Generated Knowledge + Exemplars: For more complex tasks, LLMs first generate high-level knowledge or tutorials related to the core concepts of the problem. This step aids in creating more aligned and insightful exemplars before solving the initial problem.
The authors provide detailed technical considerations, such as encouraging diversity in generated exemplars and structuring prompts for better LLM response coherence.
Experimental Results
The proposed method is empirically validated across several datasets, including GSM8K, MATH, Codeforces, and BIG-Bench reasoning tasks. The findings reveal that analogical prompting consistently outperforms existing methods, including 0-shot CoT and few-shot CoT.
- For GSM8K, the approach led to a notable average accuracy gain, outperforming both 0-shot CoT and few-shot CoT.
- In MATH problem-solving, tailored exemplars significantly boosted performance, particularly in diverse reasoning types such as algebra and geometry.
- The Codeforces experiments highlighted the method's efficacy, where generating both knowledge and exemplars helped tackle complex algorithmic challenges.
- Across BIG-Bench reasoning tasks, analogical prompting showed robust improvements in accuracy, underscoring its versatility.
Implications and Future Directions
This research has both practical and theoretical implications:
- Practical Applications: The elimination of manual labeling and the generation of highly relevant exemplars make this approach highly practical for real-world applications in education, automated tutoring, and complex problem-solving tasks.
- Theoretical Advancement: The success of analogical prompting in LLMs informs broader AI research about the potential of incorporating cognitive reasoning strategies into models, thus bridging a gap between human-like problem-solving and artificial intelligence.
Future work could explore more sophisticated structuring of prompts and the integration of analogical reasoning with other advanced CoT techniques like self-consistency. Additionally, expanding the range of problems and exploring cross-task generalization could further cement analogical prompting as a key strategy in the evolving landscape of LLMs.
Conclusion
The paper presents a compelling case for analogical prompting as a significant enhancement to the reasoning capabilities of LLMs. By mimicking human cognitive processes, this methodology not only improves accuracy across a variety of tasks but also offers a scalable solution free from the constraints of manual data labeling. The experimental results are a testament to the efficacy of this innovative approach, setting a precedent for future research in AI-driven reasoning.