Abstract
A described technology can implement a real-time confidence scoring pipeline that operates concurrently with a model's text generation process. This system can analyze internal model signals, for example, logit entropy, hidden state activations, and retrieval-augmented generation grounding, to calculate a factual confidence score for discrete spans of text. The scores can then be used by a user interface on a computing device (e.g., a smartphone, laptop, or wearable device) to apply visual treatments, such as color-coded highlighting or interactive tooltips, which can provide users with granular, real-time feedback on the potential reliability of the generated content.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Kumar, Pankaj, "Real-Time Scoring and Visualization of Span-Level Confidence for Generated Text", Technical Disclosure Commons, (October 12, 2026)
https://www.tdcommons.org/dpubs_series/12092