2 articles tagged with #llm-explainability. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce Inference-Time Argumentation (ITA), a neurosymbolic framework that combines large language models with formal argumentation semantics for claim verification. The system generates arguments, scores them, and produces ternary (true/false/uncertain) predictions with faithful, inspectable reasoning structures rather than post-hoc justifications.
Researchers propose π-Soft-NC and π-Soft-NS, improved evaluation metrics for assessing input attribution methods in large language models that control for the number of retained words, addressing a fundamental bias in existing faithfulness evaluation frameworks. They also introduce Grad-ELLM, a gradient-based attribution method designed for decoder-only LLMs that combines gradient and attention mechanisms for stronger explanatory performance.
