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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Exploring Computational User Models for Agent Policy Summarization.

Isaac Lage1, Daphna Lifschitz2, Finale Doshi-Velez1

  • 1Harvard University.

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Summary
This summary is machine-generated.

Understanding AI behavior is crucial. This study shows matching AI policy summarization models to how users reconstruct policies improves understanding and performance.

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • AI agents are increasingly used in high-stakes decision-making.
  • Understanding AI agent behavior is critical for user trust and safety.
  • Current policy summarization methods often assume a fixed user model for policy reconstruction.

Purpose of the Study:

  • To explore the impact of different models on AI policy summarization.
  • To introduce an imitation learning-based approach for policy summarization.
  • To investigate how user models influence policy reconstruction and summarization effectiveness.

Main Methods:

  • Developed an imitation learning-based approach for policy summarization.
  • Conducted computational simulations to assess policy reconstruction quality with varying models.
  • Performed a human-subject study to evaluate user policy reconstruction in different contexts.

Main Results:

  • A mismatch between summary extraction and policy reconstruction models degrades performance.
  • Users employ different models for policy reconstruction depending on the context.
  • Matching the summary extraction model to user context-specific models improves performance.

Conclusions:

  • Careful consideration of user models is essential for effective AI policy summarization.
  • Tailoring summarization models to user cognitive processes enhances AI interpretability.
  • This research highlights the importance of personalized approaches in human-AI interaction.