Reasoning Large Language Model (LLM)

A Reasoning Large Language Model (LLM) is a large language model that applies reasoning steps during LLM inference.

• Context:
  • It can range from handling simple logical tasks (e.g., basic mathematical reasoning) to more complex problem-solving (e.g., causal inference or counterfactual reasoning).
  • It can leverage Rationale Engineering techniques to refine, explore, and verify rationales during problem-solving.
  • It can demonstrate reasoning abilities by applying knowledge to novel situations or dynamic environments.
  • It can provide insights into specific domains such as scientific research, educational tools, or complex decision-making processes.
  • It can encounter challenges with reasoning consistency and may generate plausible but incorrect explanations.
  • ...
• Example(s):
  • The OpenAI o1-preview.
  • One that implements Chain-of-Thought Prompting to solve multi-step word problems in math and logic.
  • One that using Tree-of-Thought to explore various paths in strategic decision-making tasks.
  • ...
• Counter-Example(s):
  • A basic Text Generation Model, which focuses on producing fluent text without logical reasoning capabilities.
  • A Chatbot designed for simple Q&A, lacking advanced reasoning mechanisms or logical inference.
• See: Large Language Model, Chain-of-Thought Prompting, Causal Inference, Rationale Engineering, Emergent Ability, Chain-of-Thought Prompting, Self-Taught Reasoner (STaR), Hybrid Reasoning Approach, Reasoning-Enhanced Fine-Tuning, Symbolic Reasoning, Commonsense Reasoning, Arithmetic Reasoning, Bootstrapping Models, Reasoning Benchmarks

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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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