F1 Score
The harmonic mean of precision and recall, used to measure the overall accuracy of a classification or evaluation method.
Timeline (last 90 days)
In 18 real user sessions, Agile Deliberation improved the F1 score of the Gemini-based classifier by 10.5 points compared with the zero-shot baseline.
The F1 score is the harmonic mean of precision and recall, ranging from 0 to 1 (or 0 to 100).
The F1 score is a commonly used composite evaluation metric in information retrieval and classification tasks, defined as the harmonic mean of precision and recall, with a range of 0 to 1.
Experiments on three backbone LLMs show that ExLabel achieves the best F1 score under all settings.
On the Python and multilingual subsets, ExLabel's task clarity evaluation F1 improved by 44.2% and 49.9% relative to the best baseline, respectively.
All Facts (5)
In 18 real user sessions, Agile Deliberation improved the F1 score of the Gemini-based classifier by 10.5 points compared with the zero-shot baseline.
50%UnverifiedThe F1 score is the harmonic mean of precision and recall, ranging from 0 to 1 (or 0 to 100).
50%UnverifiedThe F1 score is a commonly used composite evaluation metric in information retrieval and classification tasks, defined as the harmonic mean of precision and recall, with a range of 0 to 1.
50%UnverifiedExperiments on three backbone LLMs show that ExLabel achieves the best F1 score under all settings.
50%UnverifiedOn the Python and multilingual subsets, ExLabel's task clarity evaluation F1 improved by 44.2% and 49.9% relative to the best baseline, respectively.
50%