<br>Source: *Faithful Reasoning Using Large Language Models* by Antonia Creswell et al. (2022)](https://arxiv.org/abs/2208.14271)
<br>Source: *Faithful Reasoning Using Large Language Models* by Antonia Creswell et al. (2022)](https://arxiv.org/abs/2208.14271)
@ -432,7 +432,7 @@ The method is complicated, and works as follows:
- First, build a maieutic tree, where each node is a statement that could be true or false:
- First, build a maieutic tree, where each node is a statement that could be true or false:
- Start with a multiple-choice question or true/false statement (e.g. `War cannot have a tie`)
- Start with a multiple-choice question or true/false statement (e.g. `War cannot have a tie`)
- For each possible answer to the question, use the model to generate a correponding explanation (with a prompt like `War cannot have a tie? True, because`)
- For each possible answer to the question, use the model to generate a corresponding explanation (with a prompt like `War cannot have a tie? True, because`)
- Then, prompt the model with the question and the generated explanation, and ask it to produce the answer. If reversing the explanation (with a prefix like `It is wrong to say that {explanation}`) reverses the answer, then the explanation is considered 'logically integral.'
- Then, prompt the model with the question and the generated explanation, and ask it to produce the answer. If reversing the explanation (with a prefix like `It is wrong to say that {explanation}`) reverses the answer, then the explanation is considered 'logically integral.'
- If an explanation is not logically integral, then repeat the above process recursively, with each explanation turned into a True or False question, and generate more explanations for each new question.
- If an explanation is not logically integral, then repeat the above process recursively, with each explanation turned into a True or False question, and generate more explanations for each new question.
- After all of the recursive explaining is done, you end up with a tree of explanations, where each leaf on the tree has the property that reversing the explanation reverses the model's answer.
- After all of the recursive explaining is done, you end up with a tree of explanations, where each leaf on the tree has the property that reversing the explanation reverses the model's answer.