Reasoning LLM-based AI Model

A Reasoning LLM-based AI Model is a linguistic AI model that applies reasoning steps during LLM inference (designed to perform logical thinking and problem solving through structured reasoning processes).

• AKA: Thinking LLM.
• Context:
  • It can (typically) incorporate Reasoning Techniques such as:
    • Chain Of Thought Prompting for chain Of thought reasoning through step decomposition.
    • Tree Of Thoughts for multiple path exploration.
    • Rationale Engineering for reasoning verification.
    • Self Taught Reasoning for capability improvement.
  • It can (often) execute Natural Language Program through code generation.
  • It can (often) conduct Probabilistic Reasoning through statistical inference.
  • It can (often) utilize Rationale Engineering through reasoning refinement.
  • It can (often) implement Tree Of Thoughts through path exploration.
  • It can (often) perform Meta Reasoning through method selection.
  • ...
  • It can range from being a Basic Reasoning Model to being an Advanced Reasoning Model, depending on its model complexity.
  • It can range from being a Simple Logical Reasoner to being a Complex Problem Solver, depending on its reasoning capability.
  • It can range from being a Domain Specific Reasoner to being a General Purpose Reasoner, depending on its application scope.
  • ...
  • It can leverage External Tool through integration interfaces.
  • It can handle Causal Inference through logical analysis.
  • It can explore Counterfactual Reasoning through alternative scenarios.
  • ...
• Example(s):
  • Commercial Implementations, such as:
    • OpenAI o1 (2024) with specialized reasoning.
    • OpenAI o3 (2024) with adaptive thinking.
    • Claude 3 (2024) with constitutional reasoning.
  • Framework Implementations, such as:
    • Chain Of Thought Models for mathematical problems.
    • Tree Of Thought Models for strategic planning.
    • Rationale Enhanced Models for scientific research.
  • ...
• Counter-Example(s):
  • Basic Text Generation Model, which lacks structured reasoning capability.
  • Simple Chatbot, which focuses on pattern matching over logical inference.
  • Pure Retrieval Model, which emphasizes information lookup over reasoning processes.
  • Pattern Recognition Model, which relies on statistical correlation rather than causal understanding.
• See: Chain Of Thought Processing, Tree Of Thoughts, Rationale Engineering, Causal Inference, Meta Reasoning, Symbolic Reasoning, Commonsense Reasoning, Arithmetic Reasoning, Reasoning Benchmark, Self Taught Reasoner, Hybrid Reasoning Approach.

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References

2024

• Perplexity.ai
  • A reasoning LLM (Large Language Model) is an advanced AI model designed to perform logical thinking, problem-solving, and inference tasks beyond simple text generation. Here are the key aspects of a reasoning LLM:
  • Capabilities
    • Break down complex problems into smaller, manageable steps
    • Draw logical conclusions based on given information
    • Provide step-by-step explanations for solutions
    • Apply knowledge to novel situations
    • Engage in causal reasoning and counterfactual thinking
  • Techniques
    • Chain-of-Thought Prompting: Encourages the model to show its work by generating intermediate steps[1].
    • Tree-of-Thoughts: Allows the model to explore multiple reasoning paths before reaching a conclusion[2].
    • Rationale Engineering: Improves the elicitation and use of reasoning through refinement, exploration, and verification of rationales[1].
  • Limitations
    • Lack of genuine understanding of concepts
    • Difficulty with novel situations outside training data
    • Inconsistent performance across different types of reasoning tasks
    • Potential for generating plausible but incorrect explanations
  • Applications
    • Scientific research and scientific discovery
    • Complex decision-making processes
    • Educational tools for problem-solving
    • Enhancing AI assistants for more sophisticated tasks
  • Ongoing Research
    • Meta-reasoning capabilities to dynamically select appropriate reasoning methods.[5]
    • Integration of external knowledge bases and external tools
    • Improvement of reasoning consistency and reasoing reliability
  • Citations:

[1] https://blog.paperspace.com/understanding-reasoning-in-llms/
[2] https://www.promptingguide.ai/research/llm-reasoning
[3] https://www.shaped.ai/blog/do-large-language-models-llms-reason
[4] https://www.finn-group.com/post/the-great-debate-do-language-models-reason
[5] https://arxiv.org/html/2406.11698v1
[6] https://www.reddit.com/r/MachineLearning/comments/1330rbb/d_knowledge_vs_reasoning_in_llms/
[7] https://huggingface.co/blog/KnutJaegersberg/active-reasoning
[8] https://www.kaggle.com/code/flaussy/large-language-models-reasoning-ability

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