Large Language Models are Contrastive Reasoners (2403.08211v3)

Abstract: Prompting methods play a crucial role in enhancing the capabilities of pre-trained LLMs. We explore how contrastive prompting (CP) significantly improves the ability of LLMs to perform complex reasoning. We demonstrate that LLMs are decent contrastive reasoners by simply adding "Let's give a correct and a wrong answer." before LLMs provide answers. Experiments on various LLMs show that zero-shot contrastive prompting improves the performance of standard zero-shot prompting on a range of arithmetic, commonsense, and symbolic reasoning tasks without any hand-crafted few-shot examples, such as increasing the accuracy on GSM8K from 35.9% to 88.8% and AQUA-RAT from 41.3% to 62.2% with the state-of-the-art GPT-4 model. Our method not only surpasses zero-shot CoT and few-shot CoT in most arithmetic and commonsense reasoning tasks but also can seamlessly integrate with existing prompting methods, resulting in improved or comparable results when compared to state-of-the-art methods. Our code is available at https://github.com/yao8839836/cp

• The paper introduces Contrastive Prompting, a novel strategy enhancing LLM reasoning by directing models to generate both correct and incorrect responses.
• The paper demonstrates significant performance gains, notably increasing GSM8K accuracy from 35.9% to 88.8% and AQUA-RAT from 41.3% to 62.2% with GPT-4.
• The paper highlights Contrastive Prompting’s advantage over traditional Chain-of-Thought approaches by reducing the need for manual few-shot examples and improving error analysis.

Enhancing LLMs' Reasoning Capabilities through Contrastive Prompting

Introduction to Contrastive Prompting

Recent developments in the domain of LLMs have showcased their potential to tackle a wide array of complex reasoning tasks. However, the quest to further refine their reasoning and problem-solving abilities continues. This exploration introduces a novel prompting strategy, termed as "Contrastive Prompting" (CP), aimed at significantly enhancing the reasoning capabilities of LLMs, such as GPT-4, across a spectrum of tasks including arithmetic, commonsense, and symbolic reasoning. By integrating a directive to generate both correct and incorrect responses within their outputs, CP marks a notable advancement in prompting methodologies. This method has demonstrated substantial improvements in performance metrics, for instance, escalating the accuracy in tasks like GSM8K from 35.9% to 88.8% and in AQUA-RAT from 41.3% to 62.2% using GPT-4, without the need for manual few-shot examples.

Addressing Challenges in Current Prompting Paradigms

The emergence of CP is situated against the backdrop of existing prompting techniques, specifically Chain-of-Thought (CoT) prompting, which has shown promise but also faces limitations such as generating inaccurate reasoning steps or needing labor-intensive manual labeling for diverse tasks. CP sidesteps these hurdles by autonomously guiding LLMs to generate both correct and incorrect outcomes, thus enriching their self-evaluation capabilities and fostering a deeper understanding of the tasks at hand.

Methodology: Implementing Contrastive Prompting

The CP technique is structured around a two-stage prompting process: firstly, prompting the model to articulate a reasoning process that culminates in both a correct and an incorrect answer, and subsequently, extracting the correct answer from this generated response. Such a framework not only negates the necessity for pre-labeled examples but also inherently encourages models to discern and analyze potential errors within their reasoning processes.

Experimental Validation and Insights

The robust evaluation of CP across twelve datasets involving arithmetic, commonsense, symbolic, and other logical reasoning tasks underpins its efficacy. Notably, CP outperforms existing zero-shot and few-shot CoT methods in most instances and exhibits compatibility with state-of-the-art techniques, suggesting its potential as a universal enhancement to current LLM prompting strategies.

Comparative Analysis with Current Methodologies

When compared with a range of baseline methods, including Few-shot-CoT and several state-of-the-art prompting strategies, CP demonstrates superior or comparable performance. Its integration with Few-shot-CoT particularly shines, achieving new benchmarks on datasets like GSM8K, AQUA-RAT, and SVAMP with GPT-4. This comparative analysis solidifies CP's position as a highly effective approach for improving LLMs' reasoning capabilities.

Theoretical Implications and Future Trajectories

The CP method's success can be attributed to its alignment with the intrinsic learning mechanisms of LLMs, potentially tapping into the patterns formed during their extensive pre-training on diverse textual data. Looking ahead, there is ample scope for investigating CP's application across various model sizes, mitigating potential biases in generated content, and exploring synergies with other advanced prompting techniques. Additionally, an in-depth analysis of CP's impact on the internal parameters of LLMs could offer further insights into the underpinnings of its effectiveness.

Conclusion

The introduction of Contrastive Prompting heralds a significant step forward in the refinement of LLMs for complex reasoning tasks. By enabling models to generate and evaluate both correct and incorrect answers, CP not only enhances their accuracy across a broad spectrum of challenges but also opens new avenues for research into more efficient and effective prompting techniques. The practical and theoretical implications of this methodology pave the way for future breakthroughs in the ever-evolving landscape of generative AI and LLMs.