Researchers have developed a novel approach called Logic-of-Thought (LoT) that significantly enhances the logical reasoning capabilities of large language models (LLMs).
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Researchers have developed a novel approach called Logic-of-Thought (LoT) that significantly enhances the logical reasoning capabilities of large language models (LLMs).
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Here are the steps on how Logic-of-Thought (LoT) is implemented:
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Here are the steps on how Logic-of-Thought (LoT) is implemented:
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-- 1. Logic Extraction
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-- 1. Logic Extraction
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1. Use Large Language Models (LLMs) to identify sentences containing conditional reasoning relationships from the input context.
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1. Use Large Language Models (LLMs) to identify sentences containing conditional reasoning relationships from the input context.
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2. Generate a collection of sentences with logical relationships.
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2. Generate a collection of sentences with logical relationships.
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3. Use LLMs to extract the set of propositional symbols and logical expressions from the collection.
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3. Use LLMs to extract the set of propositional symbols and logical expressions from the collection.
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4. Identify propositions with similar meanings and represent them using identical propositional symbols.
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4. Identify propositions with similar meanings and represent them using identical propositional symbols.
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5. Analyze the logical relationships between propositions based on their natural language descriptions.
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5. Analyze the logical relationships between propositions based on their natural language descriptions.
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6. Add negation (¬) for propositions that express opposite meanings.
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6. Add negation (¬) for propositions that express opposite meanings.
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7. Use implication (→) to connect propositional symbols when a conditional relationship exists.
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7. Use implication (→) to connect propositional symbols when a conditional relationship exists.
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-- 2. Logic Extension
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-- 2. Logic Extension
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1. Apply logical reasoning laws to the collection of logical expressions from the Logic Extraction phase.
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1. Apply logical reasoning laws to the collection of logical expressions from the Logic Extraction phase.
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2. Use a Python program to implement logical deduction and expand the expressions.
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2. Use a Python program to implement logical deduction and expand the expressions.
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3. Apply logical laws such as Double Negation, Contraposition, and Transitivity to derive new logical expressions.
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3. Apply logical laws such as Double Negation, Contraposition, and Transitivity to derive new logical expressions.
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-- 3. Logic Translation
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-- 3. Logic Translation
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1. Use LLMs to translate the newly generated logical expressions into natural language descriptions.
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1. Use LLMs to translate the newly generated logical expressions into natural language descriptions.
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2. Combine the natural language descriptions of propositional symbols according to the extended logical expressions.
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2. Combine the natural language descriptions of propositional symbols according to the extended logical expressions.
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3. Incorporate the translated logical information as a new part of the original input prompt.
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3. Incorporate the translated logical information as a new part of the original input prompt.
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-- 4. Integration with Existing Prompting Methods
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-- 4. Integration with Existing Prompting Methods
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1. Combine the LoT-generated logical information with the original prompt.
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1. Combine the LoT-generated logical information with the original prompt.
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2. Use this enhanced prompt with existing prompting methods like Chain-of-Thought (CoT), Self-Consistency (SC), or Tree-of-Thoughts (ToT).
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2. Use this enhanced prompt with existing prompting methods like Chain-of-Thought (CoT), Self-Consistency (SC), or Tree-of-Thoughts (ToT).
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3. Feed the augmented prompt to the LLM to generate the final answer.
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3. Feed the augmented prompt to the LLM to generate the final answer.
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What do you think about LoT?
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What do you think about LoT?
Researchers have developed a novel approach called Logic-of-Thought (LoT) that significantly enhances the logical reasoning capabilities of large language models (LLMs).

Here are the steps on how Logic-of-Thought (LoT) is implemented:

-- 1. Logic Extraction

1. Use Large Language Models (LLMs) to identify sentences containing conditional reasoning relationships from the input context.
2. Generate a collection of sentences with logical relationships.
3. Use LLMs to extract the set of propositional symbols and logical expressions from the collection.
4. Identify propositions with similar meanings and represent them using identical propositional symbols.
5. Analyze the logical relationships between propositions based on their natural language descriptions.
6. Add negation (¬) for propositions that express opposite meanings.
7. Use implication (→) to connect propositional symbols when a conditional relationship exists.

-- 2. Logic Extension

1. Apply logical reasoning laws to the collection of logical expressions from the Logic Extraction phase.
2. Use a Python program to implement logical deduction and expand the expressions.
3. Apply logical laws such as Double Negation, Contraposition, and Transitivity to derive new logical expressions.

-- 3. Logic Translation

1. Use LLMs to translate the newly generated logical expressions into natural language descriptions.
2. Combine the natural language descriptions of propositional symbols according to the extended logical expressions.
3. Incorporate the translated logical information as a new part of the original input prompt.

-- 4. Integration with Existing Prompting Methods

1. Combine the LoT-generated logical information with the original prompt.
2. Use this enhanced prompt with existing prompting methods like Chain-of-Thought (CoT), Self-Consistency (SC), or Tree-of-Thoughts (ToT).
3. Feed the augmented prompt to the LLM to generate the final answer.

What do you think about LoT?
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