2 citations · 2 across the 3 of their papers we have counts for
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cs.AI2026
ActMap: Single-Pass Uncertainty Quantification from Generation-Time Activation Maps
Jacopo Dardini, Roberta Calegari
Practical uncertainty quantification (UQ) for large language models must decide, from a single generation, whether a specific answer should be trusted. Existing methods either samp…
cs.AI2022★ 2 cited
Evaluation Metrics for Symbolic Knowledge Extracted from Machine Learning Black Boxes: A Discussion Paper
Federico Sabbatini, Roberta Calegari
As opaque decision systems are being increasingly adopted in almost any application field, issues about their lack of transparency and human readability are a concrete concern for…
cs.AI2022
Clustering-Based Approaches for Symbolic Knowledge Extraction
Federico Sabbatini, Roberta Calegari
Opaque models belonging to the machine learning world are ever more exploited in the most different application areas. These models, acting as black boxes (BB) from the human persp…