Records found: 8
SDialog is an open-source Python toolkit that standardizes dialog representation and provides persona-driven simulation, orchestration pipelines, evaluation metrics, and mechanistic interpretability for LLM agents.
'OpenAI developed a weight-sparse transformer that is far more interpretable than typical LLMs, enabling researchers to trace exact internal circuits that implement simple algorithms. While much smaller and slower than state-of-the-art models, this work could illuminate how larger models reason and fail.'
'A practical tutorial showing how to build a robust ML pipeline for the diabetes dataset, evaluate and interpret it with permutation importance and PDP, and use Gemini as an AI collaborator for summaries, risks, and EDA snippets.'
'PadChest-GR pairs sentence-level bilingual radiology text with spatially grounded chest X-ray annotations to improve model interpretability and reduce hallucinations.'
Scientists are leveraging AI neural networks to predict human behavior and explore the workings of the human mind, but challenges remain in interpreting these complex models.
Thought Anchors is a new framework that improves understanding of reasoning processes in large language models by analyzing sentence-level contributions and causal impacts.
Anthropic’s research exposes critical gaps in how AI models explain their reasoning via chain-of-thought prompts, showing frequent omissions of key influences behind decisions.
Fudan University researchers have developed Lorsa, a sparse attention mechanism that disentangles atomic attention units hidden in transformer superposition, enhancing interpretability of large language models.