A Distributional Framework for Behavioral Characterization and Risk Management of Large Language Models

Key points

• Repeated executions under the same experimental conditions may produce different responses.1
• We propose a framework that can be applied across the model lifecycle, from task design to residual behavior and continuous monitoring.1
• Semantic interventions are introduced to study how these distributions change.1
• We illustrate the framework with two different tasks, borrower classification and music album recommendation, and quantify the resulting distributional changes.1

Sources (1)

• [1]A Distributional Framework for Behavioral Characterization and Risk Management of Large Language Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 08:57 PM

  Large language models are stochastic by nature.

  Repeated executions under the same experimental conditions may produce different responses.

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