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The `Combine(·)` function, used to aggregate multiple predictions in prompt ensembling, can be implemented through various strategies. Examples include non-probabilistic methods like selecting the final output via voting among the different predictions, or by choosing the prediction that shares the most content or overlap with the other candidates.

Combination Strategies for Prompt Ensembling

A financial tech company is considering two methods for combining multiple language model outputs into a single, final answer. Review the case study and the proposed methods, then recommend the most suitable method and justify your choice.

**Proposed Methods:**
1.  **Majority Voting:** Select the exact text string that appears most frequently among the generated outputs.
2.  **Maximum Content Overlap:** Select the output that shares the most common words and phrases with the other generated outputs.

Which combination strategy would you recommend for this task? Justify your recommendation by explaining the primary weakness of the *other* strategy in this specific context.

Choosing an Aggregation Strategy for Financial Data Extraction

An ensembling technique is used to improve the reliability of a language model's answer to the question, 'What is the capital of Australia?'. The model generates the following three candidate answers from three different prompts:

*   **Candidate 1:** 'The capital of Australia is Canberra.'
*   **Candidate 2:** 'Canberra is the capital city of Australia.'
*   **Candidate 3:** 'Sydney is the capital of Australia.'

Analyze the likely final output if the system uses the following two distinct combination strategies. Provide a brief justification for each.
1.  A strict majority voting method (where only identical answers count as a vote).
2.  A method that selects the candidate sharing the most content overlap with the others.

Applying Combination Strategies in Prompt Ensembling

A developer is using a technique to improve the reliability of a language model's output. For a single request to summarize a text about cellular biology, the model generates the four different one-sentence summaries listed below. After processing these four candidates, the system selects 'Summary A' as the final output.

*   **Summary A:** The mitochondrion is the cell's power plant, generating ATP through cellular respiration.
*   **Summary B:** Often called the powerhouse of the cell, the mit

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