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Applying Combination Strategies in Prompt Ensembling
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.
- A strict majority voting method (where only identical answers count as a vote).
- A method that selects the candidate sharing the most content overlap with the others.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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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 mitochondrion produces the energy currency, ATP.
- Summary C: The primary role of the ribosome is to synthesize proteins for the cell.
- Summary D: The mitochondrion, known as the cell's powerhouse, is responsible for creating ATP.
Based on the provided summaries and the final selected output, which combination method was most likely used to aggregate the candidates?
Choosing an Aggregation Strategy for Financial Data Extraction
Applying Combination Strategies in Prompt Ensembling