A company deploys a powerful language model for an internal search tool. They find that employees get good results when they type full questions like, 'Can you find the quarterly report for the sales department from last year?' However, the model performs poorly when employees use short, keyword-style queries like 'Q3 sales report'. What is the most effective and scalable strategy to specifically train the model to correctly interpret and act upon these simplified, unconventional instructions?
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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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Adapting a Chatbot for Informal User Instructions
A company deploys a powerful language model for an internal search tool. They find that employees get good results when they type full questions like, 'Can you find the quarterly report for the sales department from last year?' However, the model performs poorly when employees use short, keyword-style queries like 'Q3 sales report'. What is the most effective and scalable strategy to specifically train the model to correctly interpret and act upon these simplified, unconventional instructions?
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