Complexity of Generalization due to Instruction and Input Variation
The challenge of achieving strong generalization in instruction-tuned models is significantly complicated by the need to handle variations across two dimensions: the instructions themselves and the user inputs. To generalize effectively, a model must learn from an extensive and diverse range of tasks, each with its own set of associated input-output examples.
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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
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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
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.
Learn After
An AI team is building a general-purpose chatbot. They train two different models on a large dataset of text summarization tasks.
- Model A is trained using 100,000 different articles, but every training example uses the exact same instruction: "Summarize the following text."
- Model B is trained using only 10,000 different articles, but the training examples use 1,000 varied instructions for summarization (e.g., "Give me the gist," "What are the key points?," "Provide a brief overv
Diagnosing Generalization Failure in a Legal AI
Diagnosing a Model's Generalization Failure