System-level explainability is a post-hoc technique that interprets the attention mechanisms of language models without affecting their learning process by connecting attention patterns to concepts from understandable knowledge repositories.

Authors

• Person: Not available Organization: arXiv
  Building Trustworthy NeuroSymbolic AI Systems - arXiv

Sources

• Building Trustworthy NeuroSymbolic AI Systems - arXiv arxiv.org arXiv via serper

Referenced by nodes (2)

• Language Model concept
• attention mechanism concept