Reasoning with Language Model Prompting: A Survey

Original Paper: https://arxiv.org/abs/2212.09597

By: Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen

Abstract:

Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc. This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting. We introduce research works with comparisons and summaries and provide systematic resources to help beginners. We also discuss the potential reasons for emerging such reasoning abilities and highlight future research directions. Resources are available at

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Summary Notes

A Practical Guide to Enhancing AI Reasoning with Language Model Prompting

As the field of artificial intelligence (AI), particularly natural language processing (NLP), continues to advance, the quest to equip AI models with human-like reasoning abilities remains a significant challenge.

However, the advent of pre-trained language models has introduced new opportunities for improving these reasoning capabilities. This guide aims to provide AI engineers, especially those working in enterprise settings, with a clear understanding of how language model prompting can be utilized to enhance reasoning skills in AI models.

Key Concepts in Language Model Prompting

Prompting a language model essentially means presenting it with a task or question in a manner that encourages the generation of the desired answer. This approach is invaluable for reasoning tasks where the model needs to draw inferences or deductions from the provided information. Here’s a breakdown of the essentials:

• Standard Prompting: Involves posing a reasoning question alongside a well-designed prompt to guide the model towards the correct response.
• Few-Shot Prompting: Incorporates examples of question-answer pairs in the prompt to help the model grasp the task's context, enhancing its reasoning capability.
• Inclusion of Reasoning Steps: Embedding reasoning steps within prompts can greatly aid the model in tackling complex reasoning tasks by outlining a clearer logical path.

Enhancing Reasoning: A Taxonomy of Methods

Prompting methods can be broadly categorized into two groups for enhancing reasoning abilities:

Strategy Enhanced Reasoning

• Prompt Engineering: Fine-tuning the effectiveness of prompts.
• Process Optimization: Involves methods like self, ensemble, and iterative optimization to boost reasoning accuracy.
• External Engines: Using external computational tools for complex reasoning support.

Knowledge Enhanced Reasoning

• Leveraging Implicit and Explicit Knowledge: Drawing on the model’s internal knowledge and external information to improve reasoning.

Practical Advice for AI Engineers

• Explore Prompt Structures: Test different prompting strategies to find what works best for various tasks.
• Iterative Refinement: Continuously tweak prompts based on performance feedback to sharpen reasoning accuracy.
• Incorporate External Knowledge: For complex reasoning, integrating external information can provide valuable context.
• Use Multimodal Data: Including non-textual data (like graphs or charts) can enrich reasoning in tasks requiring broader understanding.
• Track Model Performance: Observe the effects of different prompting strategies on performance across tasks and data sets.

Facing the Challenges and Looking Forward

Despite progress, challenges such as refining reasoning processes, ensuring robustness and scalability, and efficiently integrating external knowledge need to be addressed. The future, however, looks promising with potential advancements in multi-modal reasoning and dynamic prompting strategies.

Conclusion

Language model prompting is paving the way for AI models with improved reasoning abilities. By applying effective prompting strategies, AI engineers can push the boundaries of what AI can achieve in terms of reasoning, moving closer to replicating human-like understanding. Success in this area hinges on experimentation, ongoing refinement, and keeping up with the latest research developments.

As AI practitioners embrace these practices, the potential to develop more intelligent AI systems for the future is immense.