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Critique of a Data Sourcing Strategy
Analyze the data collection workflow described in the case study. Identify the most critical flaw in this process and explain the most likely negative consequence for the AI model's future performance.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Evaluation in Bloom's Taxonomy
Cognitive Psychology
Psychology
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Critique of a Data Sourcing Strategy
A team is building a dataset to improve a language model's ability to answer questions about a new software product. They plan to collect data from early users. Arrange the following stages into the correct sequence for their data collection and refinement process.
A startup is developing a specialized chatbot for financial advice. To improve its performance, they implement the following data collection process: 1) They invite a group of beta testers to ask the chatbot any financial question they can think of. 2) They use their base language model to automatically generate an answer for each question. 3) They add these question-answer pairs directly to their fine-tuning dataset. What is the most significant weakness in this workflow that could compromise the quality of the final model?