A large language model has been fine-tuned on a variety of instructional tasks. Match each of the following performance observations with the specific type of generalization challenge it represents.
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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
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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Two Levels of Generalization in Instruction-Tuned LLMs
Complexity of Generalization due to Instruction and Input Variation
A development team fine-tunes a large language model to be a helpful assistant for summarizing legal documents. They use a large dataset of legal texts and their corresponding summaries. After deployment, they observe the following:
- The model performs exceptionally well when asked to summarize new, unseen legal documents (e.g., contracts, court rulings).
- However, when users give it slightly different instructions, such as 'Explain this legal clause in simple terms,' 'Extract the key dates
Evaluating Fine-Tuning Strategies for Generalization
Performance Metric for Instruction-Tuned LLMs
Formal Representation of an Instruction-Tuned LLM