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Diagnosing a Fine-Tuning Problem
A company fine-tuned a pre-trained language model to create a customer service chatbot. They used a large dataset of complete, unedited chat transcripts between human agents and customers. After training, they observed that while the model can generate human-like conversational text, it often fails to directly and concisely answer specific user questions (e.g., 'What are your shipping fees?'). Analyze the likely reason for the model's failure to follow instructions effectively and describe the key characteristic of the data that should have been used instead.
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Ch.2 Generative Models - 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
Related
A development team has a large, pre-trained language model that is proficient at predicting the next word in a sentence but is not effective at following direct user commands. The team's goal is to adapt the model to function as a helpful assistant that can answer a wide variety of questions directly and accurately. Which of the following datasets would be most effective for adapting the model to this new role?
Diagnosing a Fine-Tuning Problem
Analyzing a Fine-Tuning Dataset's Limitations