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Add new task: Detect Reasoning #2

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83 changes: 83 additions & 0 deletions tasks.md
Original file line number Diff line number Diff line change
Expand Up @@ -1021,4 +1021,87 @@ This timeline suggests a rapidly growing company:
4. Three years post-IPO, they expanded internationally, showing continued growth and market expansion.
Overall, this timeline depicts a successful startup that achieved significant milestones roughly every 2-3 years, culminating in international expansion within a decade of founding.

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Name: Detect the Presence of Reasoning in Messages

Description: This task involves detecting various patterns of reasoning in text messages.

Types of Reasoning:

- Deductive: Disjunctive Syllogism, Hypothetical Syllogism, Constructive Dilemma
- Inductive: Generalization, Statistical Syllogism, Argument from Analogy
- Abductive: Inference to the Best Explanation
- Causal: Cause to Effect, Effect to Cause
- Conditional: Equivalence

Example(s):

DEDUCTIVE REASONING

- Example 1: Modus Ponens
Input: If the weather is sunny, then we will go to the beach. The weather is sunny.
Output: Modus Ponens (Affirming the Antecedent): The conclusion follows logically from the given premises.

- Example 2: Modus Tollens
Input: If the email was sent, I would have received it. I did not receive the email.
Output: Modus Tollens (Denying the Consequent): If the email was sent, I would have received it. I did not receive the email, hence it was not sent.

- Example 3: Disjunctive Syllogism
Input: Either I will go for a run or I will stay home. I am not going for a run.
Output: Disjunctive Syllogism: Since you are not going for a run, you will stay home.

- Example 4: Hypothetical Syllogism
Input: If it rains, the ground will be wet. If the ground is wet, the baseball game will be canceled.
Output: Hypothetical Syllogism: If it rains, the baseball game will be canceled.

- Example 5: Constructive Dilemma
Input: If I get a job, I will move to the city. If I don't get a job, I will stay in my hometown. I will either get a job or not.
Output: Constructive Dilemma: I will either move to the city or stay in my hometown.

INDUCTIVE REASONING

- Example 6: Generalization
Input: All the dogs I've met have been friendly. Therefore, all dogs are friendly.
Output: Generalization: Based on the observed pattern, the conclusion is that all dogs are friendly.

- Example 7: Statistical Syllogism
Input: Most students who study hard get good grades. Jane studies hard.
Output: Statistical Syllogism: Jane is likely to get good grades.

- Example 8: Argument from Analogy
Input: The human brain works like a computer. A computer can be programmed. Therefore, the human brain can be programmed.
Output: Argument from Analogy: Since the human brain works like a computer and computers can be programmed, the human brain can be programmed.

ABDUCTIVE REASONING

- Example 9: Inference to the Best Explanation:
Input: The lights are out in the house. The best explanation is that there is a power outage.
Output: Inference to the Best Explanation: The most likely reason for the lights being out is a power outage.

CAUSAL REASONING

- Example 10: Cause to Effect
Input: Exposure to loud noise can cause hearing loss. John works in a noisy factory.
Output: Cause to Effect: John is at risk of hearing loss because he works in a noisy factory.

- Example 11: Effect to Cause
Input: The streets are wet. It likely rained last night.
Output: Effect to Cause: The wet streets indicate that it probably rained last night.

CONDITIONL REASONING

- Example 12: Equivalence Reasoning
Input: A shape is a square if and only if it has four equal sides and four right angles. This shape has four equal sides and four right angles.
Output: Equivalence Reasoning: Since the shape has four equal sides and four right angles, it is a square.

*Question: How to detect logical fallacies?*

- Ad Hominem
- Straw Man
- False Dilemma
- Circular Reasoning
- Hasty Generalization
- Others [add below]

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