Unverified50% confidenceBenchmarkExact time
Largha-150M-v1 achieves a reading of 0.906 on the typed-decision English self-evaluation (400 cases / 2000 decisions), higher than the 0.735 teacher self-consistency reference, but the dataset card flags a risk of label-specific overfitting.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/13/2026
First Seen
Valid until: 1/11/2027
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Related Claims
UnverifiedLargha-150M-v1's training mixture includes a pool of 500 error corrections, which were mined from the model's predictions on typed-decision test rows, so 0.906 is a fitting reading rather than a zero-shot result.75% similarUnverifiedAfter 15 training epochs on the demonstration dataset, the text classification model achieves an accuracy of 99%, with a total of 110,000 model parameters (16,000 for Attention, 33,000 for FFN, and the remainder for Embedding)67% similarUnverifiedAccording to Achint Srivastava's blog, Microsoft-Decision-1 had the highest accuracy in Microsoft's comparison of 36 benchmarks, with about 150,000 questions kept blind during training.65% similarUnverifiedThe test case provided in the course uses inputs of 80 and 92 to verify whether the result correctly reflects an increase of 15 percentage points.64% similarUnverifiedOn a test set of 333 articles and 1,218 prompts, Fiorillo v0.5 achieved an Expected Calibration Error (ECE) of 0.0168, well below the preset upper limit of 0.05.64% similar
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