Probing for Knowledge Attribution in Large Language Models

1. 1Build a dataset of LLM outputs with annotated knowledge sources
2. 2Run experiments to classify the dominant knowledge source behind each output
3. 3Configure a contributive attribution model to identify faithfulness and factuality violations
4. 4Test the model on a held-out dataset to evaluate its performance
5. 5Apply the model to real-world LLM applications to improve factuality and reliability

AI engineers and researchers benefit from understanding knowledge attribution in LLMs to develop more accurate and trustworthy models, while data scientists can apply this knowledge to improve model performance and interpretability

💡 Classifying the dominant knowledge source behind LLM outputs is essential for developing more accurate and trustworthy models

💡 Identify knowledge sources behind LLM outputs to mitigate hallucinations and improve factuality #LLMs #AI