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Paper: Self-Consistency Improves Chain of Thought Reasoning in Language Models
Link: https://arxiv.org/abs/2203.11171v4
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The key aspects of the self-consistency prompting technique are:

Essence of the technique:
Self-consistency generates multiple reasoning paths for a given problem by sampling from a language model's decoder, then aggregates the final answers to find the most consistent one. This leverages the idea that correct reasoning paths are more likely to converge on the same answer.

Key ideas:
1. Generate multiple diverse reasoning paths instead of just one greedy path
2. Marginalize out the reasoning paths to find the most consistent final answer
3. Leverages the intuition that complex reasoning problems often have multiple valid approaches to reach the correct solution
4. Works as an unsupervised "self-ensemble" on top of a single language model

What it does:
- Improves performance on complex reasoning tasks compared to standard chain-of-thought prompting
- Provides more robust answers by considering multiple reasoning approaches
- Allows the model to recover from individual errors in specific reasoning paths

Use cases:
- Arithmetic reasoning 
- Commonsense reasoning
- Symbolic reasoning
- Complex multi-step problem-solving
- Tasks where adding rationales could potentially hurt performance

Examples from the paper:

1. Arithmetic reasoning (GSM8K dataset):

Q: Janet's ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder for $2 per egg. How much does she make every day?

Sampled Path 1:
She has 16 - 3 - 4 = 9 eggs left. So she makes $2 * 9 = $18 per day.

Sampled Path 2:
She eats 3 for breakfast, so she has 16 - 3 = 13 left. Then she bakes muffins, so she has 13 - 4 = 9 eggs left. So she has 9 eggs * $2 = $18.

Final Answer: $18

2. Commonsense reasoning (StrategyQA dataset):

Q: Yes or no: Is Albany, Georgia the most populous US Albany?

Sampled Path 1:
The most populous US Albany is Albany, New York. Thus, Albany, Georgia is not the most populous US Albany. So the answer is no.

Sampled Path 2:
Albany, Georgia has a population of about 88,000. Albany, New York has a population of about 95,000. Thus, Albany, Georgia is not the most populous US Albany. So the answer is no.

Final Answer: No

3. Physics reasoning:

Q: Identify the physics formula that would be most useful for finding the answer to each of the following word problems. A particle of mass 6.8 * 10 ^ -5 g and charge 58 mC moves in a region of space where the electric field is uniform and is 7.2 N/C in the x direction and zero in the y and z direction. If the initial velocity of the particle is given by vy = 1.7 * 10 ^ 6 m/s, vx = vz = 0, what is the speed of the particle at 0.7 s?

Sampled Path 1:
We need to find acceleration first. The formula for force is F = ma, and for electric force, F = qE. Equating these, we get ma = qE, or a = qE/m. This gives us the acceleration, which we can use to find the final velocity. The most relevant formula is F = ma or a = F/m. So the answer is F = m * a.

Sampled Path 2:
We need to find the particle's velocity after 0.7s. The particle experiences a force due to the electric field, which causes acceleration. The force is given by F = qE, and Newton's second law states F = ma. Combining these, we get qE = ma, or a = qE/m. This acceleration will change the velocity over time. The most relevant formula here is F = ma, as it relates the force to the resulting acceleration. So the answer is F = m * a.

Final Answer: F = m * a

These examples demonstrate how self-consistency generates multiple reasoning paths and aggregates them to arrive at a final answer, improving the model's problem-solving capabilities across various domains.