LLM-based Reasoner

A LLM-based Reasoner is a reasoning system that uses large language models to perform reasoning tasks.

• Context:
  • It can (often) lack strict Logical Guarantees.
  • ...
  • It can use Few-Shot Learning or Zero-Shot Learning techniques to generalize across new tasks with minimal or no task-specific training.
  • It can integrate with Retrieval-Augmented Generation (RAG) Systems.
  • It can employ Chain-of-Thought Prompting and Iterative Reasoning frameworks to guide decision-making, breaking down complex problems into smaller steps.
  • It can support exploratory tasks by balancing Exploration vs. Exploitation during reasoning workflows, enhancing efficiency in solving novel problems.
  • It can perform Multi-Step Reasoning tasks and self-verify its outputs to improve accuracy, such as generating and critiquing solutions iteratively.
  • ...
• Example(s):
  • o1 Model which demonstrates reasoning in Technical Problem-Solving and Scientific Problem-Solving.
  • Claude 3.5 Sonnet LLM which demonstrates retrieval-augmented generation.
  • GraphAgent-Reasoner: A framework for graph reasoning tasks capable of handling larger graphs (up to 1000 nodes) with high accuracy.
  • ...
• Counter-Example(s):
  • Formal Logical Reasoning Systems, which use symbolic logic to make verifiable inferences rather than probabilistic predictions.
  • Expert Systems, which rely on predefined rules and domain-specific knowledge rather than general-purpose language models.
  • Automated Theorem Provers, which strictly adhere to formal proof methods to ensure correctness.
  • Human Reasoners, who bring intuition and experience to ambiguous problems, unlike LLMs, which operate purely on learned statistical patterns.
• See: Automated Reasoning System, Transformer Model, Commonsense Reasoner, Question Answering, Chain-of-Thought Prompting, RAP (Reasoning via Planning), Exploration vs. Exploitation

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References

2024

• Perplexity.ai
  • Core Components:
    • Large Language Model: The foundation of the system, typically a powerful LLM model like GPT-3, GPT-4 LLM, or open-source alternatives such as LLaMA^[1].
    • Reasoning Framework: A structured approach to guide the LLM through complex reasoning processes. Techniques include:
      • Chain-of-thought prompting
      • Tree-of-thought reasoning
      • Iterative refinement
    • World Model: Some advanced reasoning systems incorporate an internal world model to simulate outcomes and predict states^[1].
    • Reward Function: Used to evaluate the quality of reasoning steps and guide searches for optimal solutions^[1].
  • Key Capabilities:
    • Multi-step Reasoning: Breaking down complex problems into smaller, manageable steps.
    • Exploration vs. Exploitation: Balancing between exploring new paths and exploiting known solutions^[1].
    • Self-verification: Some reasoning systems can critique and improve their own reasoning^[3].
  • Advantages:
    • Flexibility: Applicable across a wide range of reasoning tasks without requiring domain-specific training.
    • Interpretability: The step-by-step reasoning process can be inspected and understood.
    • Scalability: Performance improves with larger and more capable LLMs.
  • Challenges:
    • Deductive Reasoning: LLMs tend to be stronger at inductive reasoning than at deductive logic^[6].
    • Consistency: Ensuring reliable and reproducible results across different prompts or runs.
    • Computational Cost: Advanced reasoning techniques are often resource-intensive, especially with large models.
  • Applications:
    • Math problem-solving
    • Logical inference
    • Plan generation
    • Complex decision-making tasks
  • Recent Developments: Researchers are actively improving LLM-based reasoning systems. Notable approaches include:
    • RAP (Reasoning via Planning): Formulates reasoning as a planning problem, employing the LLM as both a world model and reasoning agent^[1].
    • GraphAgent-Reasoner: A multi-agent framework for graph reasoning tasks, capable of handling large graphs (up to 1000 nodes) with high accuracy^[5].
  • Citations:

[1] https://arxiv.org/abs/2305.14992  
[2] https://github.com/maitrix-org/llm-reasoners  
[3] https://www.llm-reasoners.net/blog  
[4] https://www.llm-reasoners.net  
[5] https://arxiv.org/html/2410.05130  
[6] https://www.reddit.com/r/LocalLLaMA/comments/1f2zyv7/apparently_llms_are_strong_inductive_reasoners/  
[7] https://www.reddit.com/r/LocalLLaMA/comments/1fey8qm/alternatives_to_llm_for_deductive_reasoning/  
[8] https://github.com/atfortes/Awesome-LLM-Reasoning  

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