LLM reasoning types and how it works

A.

Types of LLM reasoning

Deductive reasoning:

 In deductive reasoning, one draws a conclusion by assuming the validity of the premises. Since the conclusion in deductive reasoning must always flow logically from the premises, if the premises are true, then the conclusion must also be true.

Inductive reasoning: 

A conclusion is reached by inductive reasoning when supporting evidence is considered and accepted.Based on the facts provided, it is probable that the conclusion is correct, but this is by no means a guarantee.

Example:

Observation: Every time we see a creature with wings, it is a bird. Observation: We see a creature with wings. Conclusion: The creature is likely to be a bird.

Abductive reasoning:

 In abductive reasoning, one seeks the most plausible explanation for a collection of observation in order to arrive at a conclusion. This conclusion is based on the best available information and represents the most plausible explanation; nonetheless, it should not be taken as absolute fact.

For example: • Observation: The car cannot start and there is a puddle of liquid under the engine. • Conclusion: The most likely explanation is that the car has a leak in the radiator

Other types of reasoning include analogical reasoning, which involves making comparisons between two or more things in order to make inferences or arrive at conclusions; causal reasoning, which involves identifying and understanding the causes and effects of events or phenomena; and probabilistic reasoning, which involves making decisions or arriving at conclusions based on the likelihood or probability of certain outcomes.

Formal Reasoning vs Informal Reasoning: In mathematics and logic, the term “formal reasoning” refers to a certain type of reasoning that is both methodical and logical. In daily life, we often utilize a style of reasoning known as “informal reasoning,” which is a less formal method that relies on intuition, experience, and common sense to make conclusions and solve issues. While informal reasoning is more flexible and open-ended, it may be less reliable than formal reasoning due to its lack of structure.

ref https://www.digitalocean.com/community/tutorials/understanding-reasoning-in-llms

 B. 

How LLMs use reasoning:

https://www.promptingguide.ai/research/llm-reasoning

For developers

https://github.com/atfortes/Awesome-LLM-Reasoning

 

Additional links

https://openai.com/index/learning-to-reason-with-llms/

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