Prompting AI Guide

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LLM Reasoning ​

Overview ​

Over the last couple of years, large language models (LLMs) have made significant progress in a wide range of tasks. More recently, LLMs have shown the potential to exhibit reasoning abilities when scaled to a large enough size. Different types of reasoning are fundamental to intelligence but it's not fully understood how AI models can learn and harness this capability to solve complex problems. It is an area of huge focus and investment for many research labs.

Research Overview ​

Reasoning with Foundation Models ​

Sun et al. (2023) recently proposed an overview of reasoning with foundation models which focuses on the latest advancements in various reasoning tasks. This work also focuses on a more extensive look at reasoning that spans multimodal models and autonomous language agents.

Types of Reasoning Tasks ​

Reasoning tasks could include tasks such as:

  • Mathematical Reasoning: Numerical problem solving
  • Logical Reasoning: Deductive and inductive logic
  • Causal Reasoning: Understanding cause-and-effect relationships
  • Visual Reasoning: Interpreting visual information

The following figure shows an overview of reasoning tasks discussed in the survey paper, including reasoning techniques for foundation models such as alignment training and in-context learning.

"Reasoning Tasks"

Figure source: Sun et al., 2023

Eliciting Reasoning in LLMs ​

Research Categorization ​

Reasoning in LLMs can be elicited and enhanced using many different prompting approaches. Qiao et al. (2023) categorized reasoning methods research into two different branches:

  1. Reasoning Enhanced Strategy: Focuses on improving reasoning capabilities
  2. Knowledge Enhancement Reasoning: Enhances knowledge for better reasoning

Reasoning Strategies ​

Reasoning strategies include:

  • Prompt Engineering: Designing effective prompts
  • Process Optimization: Improving reasoning processes
  • External Engines: Using external tools and systems

Single-Stage Prompting ​

For instance, single-stage prompting strategies include:

  • Chain-of-Thought: Step-by-step reasoning
  • Active-Prompt: Interactive reasoning approaches

A full taxonomy of reasoning with language model prompting can be found in the paper and summarized in the figure below:

"Reasoning Taxonomy"

Figure source: Qiao et al., 2023

Techniques for Improving Reasoning ​

Comprehensive Approach ​

Huang et al. (2023) also explain a summary of techniques to improve or elicit reasoning in LLMs such as GPT-3. These techniques range from:

  • Fully Supervised Fine-tuning: Training on explanation datasets
  • Prompting Methods: Various reasoning approaches

Specific Techniques ​

Below is a summary of the techniques described in the paper:

"Reasoning Techniques"

Figure source: Huang et al., 2023

Can LLMs Reason and Plan? ​

Ongoing Debate ​

There is a lot of debate about whether LLMs can reason and plan. Both reasoning and planning are important capabilities for unlocking complex applications with LLMs such as in the domains of robotics and autonomous agents.

Position Paper Analysis ​

A position paper by Subbarao Kambhampati (2024) discusses the topic of reasoning and planning for LLMs.

Author's Conclusion ​

Here is a summary of the author's conclusion:

To summarize, nothing that I have read, verified, or done gives me any compelling reason to believe that LLMs do reasoning/planning, as normally understood. What they do instead, armed with web-scale training, is a form of universal approximate retrieval, which, as I have argued, can sometimes be mistaken for reasoning capabilities.

Key Insights ​

  1. Reasoning Potential: LLMs show promise in reasoning when properly scaled
  2. Multiple Approaches: Various techniques exist for eliciting reasoning
  3. Ongoing Research: Active area of investigation and investment
  4. Debate Continues: No consensus on true reasoning capabilities
  5. Practical Applications: Important for complex AI applications

Research Areas ​

  • Mathematical Reasoning: Numerical problem solving capabilities
  • Logical Reasoning: Deductive and inductive logic
  • Causal Reasoning: Understanding relationships
  • Visual Reasoning: Interpreting visual information
  • Planning: Sequential decision making

References ​