How Much Confidence Do We Have for GenAI-Based Reasoning for Reliability Estimation?
Bibliographic record
Abstract
This paper investigates the confidence of using GenAI-based models in performing quantitative reliability reasoning, specifically focusing on estimating the shape and scale parameters of Weibull distributions. Using synthetic datasets generated from Weibull models, we design multiple experimental factors, including sample sizes, shape parameters, prompts, and five GenAl models to see how various factors impact parameter estimations in the Weibull reliability model. The confidence levels and robustness of each GenAl model's estimates are assessed through a factorial experimental design framework. It is interesting to observe the GenAl-based parameter estimates for the Weibull model do not exhibit convergence over increasing sample size as what is commonly observed in the traditional pure statistics-based inference. We also observe that ChatGPT and DeepSeek perform the best for both shape and scale parameters' estimation compared with other GenAl models. ChatGPT and DeepSeek also show robustness in estimating parameters regardless of the types of prompts being used. The findings shed light on the reliability and confidence of using GenAl models for unknown parameter estimation in Weibull models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".