Transformers can solve in-context change-point detection with model size scaling by knowledge of the shift timing, matching optimal baselines on synthetic data and improving pretrained models on disease and financial forecasting.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
ContextualSHAP augments SHAP explanations with LLM-generated contextual text guided by user parameters, and a healthcare user study found the combined outputs rated more understandable than visuals alone.
citing papers explorer
-
In-Context Learning Under Regime Change
Transformers can solve in-context change-point detection with model size scaling by knowledge of the shift timing, matching optimal baselines on synthetic data and improving pretrained models on disease and financial forecasting.
-
ContextualSHAP : Enhancing SHAP Explanations Through Contextual Language Generation
ContextualSHAP augments SHAP explanations with LLM-generated contextual text guided by user parameters, and a healthcare user study found the combined outputs rated more understandable than visuals alone.