Training Strategy for a Legal AI
A legal tech company wants to improve its powerful, general-purpose language model ('StrongLLM') for the specific task of identifying 'indemnity clauses' in contracts. They have a massive database of unlabeled contracts but lack the resources to have lawyers label them all. They also have a smaller, less capable model ('WeakLLM') that can identify these clauses with about 70% accuracy. They propose the following two-stage plan:
- Use 'WeakLLM' to scan the entire database of unlabeled contracts and generate a label ('contains indemnity clause' or 'does not contain indemnity clause') for each one.
- Take the dataset of contracts and their machine-generated labels and use it to fine-tune the 'StrongLLM', training it to predict the labels provided by 'WeakLLM'.
Based on this scenario, explain the rationale behind this two-stage training strategy. Specifically, describe the role of the 'WeakLLM' in the first stage and the expected outcome for the 'StrongLLM' after the second stage.
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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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Training Strategy for a Legal AI
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